THIS POST IS CONTINUED FROM PART 4, BELOW--
https://ajitvadakayil.blogspot.com/2019/11/what-artificial-intelligence-cannot-do.html
Deep learning is a type
of machine learning (ML) and artificial intelligence (AI) that imitates the way
humans gain certain types of knowledge.
Deep learning is an important element
of data science, which includes statistics and predictive modeling. It is
extremely beneficial to data scientists who are tasked with collecting,
analyzing and interpreting large amounts of data; deep learning makes this
process faster and easier.
Deep learning
represents the very cutting edge of artificial intelligence (AI). Instead of
teaching computers to process and learn from data (which is how machine
learning works), with deep learning, the computer trains itself to process and
learn from data.
Deep learning (DL) is a
subset of ML, in which multiple levels of models work together at more complex
tasks, with each level of model relying on outputs from a prior level to
perform a higher-level function. .
Deep learning (also
known as deep structured learning or hierarchical learning) is part of a
broader family of machine learning methods based on artificial neural networks.
Learning can be supervised, semi-supervised or unsupervised.
The most important
difference between deep learning and traditional machine learning is its
performance as the scale of data increases. When the data is small, deep
learning algorithms don't perform that well. This is because deep learning
algorithms need a large amount of data to understand it perfectly
You need to learn
Machine Learning first then you can plan for Deep Learning or AI. Machine
Learning is mandatory to learn deep learning or AI
Deep learning is a field of study that gives computers the
ability to learn without being explicitly programme – while adding that it tends to result in higher
accuracy, require more hardware or training time, and perform exceptionally
well on machine perception tasks that involved unstructured data such as blobs
of pixels or text.
We refer to ‘deep
learning’ because the neural networks have various (deep) layers that enable
learning. Just about any problem that requires “thought” to figure out is a
problem deep learning can learn to solve.
With machine learning,
you need fewer data to train the algorithm than deep learning. Deep learning
requires an extensive and diverse set of data to identify the underlying
structure. Besides, machine learning provides a faster-trained model. Most
advanced deep learning architecture can take days to a week to train.
The
advantage of deep learning over machine learning is it is highly accurate. You
do not need to understand what features are the best representation of the
data; the neural network learned how to select critical features. In machine
learning, you need to choose for yourself what features to include in the
model.
Machine learning does
not require the same costly, high-end machines and high-performing GPUs that
deep learning does.
In the end, many data
scientists choose traditional machine learning over deep learning due to its
superior interpretability, or the ability to make sense of the solutions.
Machine learning algorithms are also preferred when the data is small.
At its simplest, deep learning can be thought of as a way to automate predictive analytics. While traditional machine learning algorithms are linear, deep learning algorithms are stacked in a hierarchy of increasing complexity and abstraction.
A type of advanced
machine learning algorithm, known as artificial neural networks, underpins most
deep learning models. As a result, deep learning may sometimes be referred to
as deep neural learning or deep neural networking.
Because deep learning
models process information in ways similar to the human brain, they can be
applied to many tasks people do. Deep learning is currently used in most common
image recognition tools, natural language processing and speech recognition
software. These tools are starting to appear in applications as diverse as
self-driving cars and language translation services.
Deep learning is a
subset of machine learning. Usually, when people use the term deep learning,
they are referring to deep artificial neural networks, and somewhat less
frequently to deep reinforcement learning.
Deep learning is a
computer software that mimics the network of neurons in a brain. -- it is called deep learning because it makes use of deep
neural networks.
The machine uses
different layers to learn from the data. The depth of the model is represented
by the number of layers in the model.
Deep learning is the
new state of the art in term of AI. In deep learning, the learning phase is
done through a neural network. A neural network is an architecture where the
layers are stacked on top of each other
The biggest limitation
of deep learning models is they learn through observations. This means they
only know what was in the data on which they trained. If a user has a small
amount of data or it comes from one specific source that is not necessarily
representative of the broader functional area, the models will not learn in a
way that is generalizable.
Deep learning requires
large amounts of data. Furthermore, the more powerful and accurate models will
need more parameters, which, in turn, requires more data.
Once trained, deep
learning models become inflexible and cannot handle multitasking. They can
deliver efficient and accurate solutions, but only to one specific problem.
Even solving a similar problem would require retraining the system.
Any application that
requires reasoning -- such as programming or applying the scientific method --
long-term planning and algorithmic-like data manipulation is completely beyond
what current deep learning techniques can do, even with large data.
Deep learning is a
particular kind of machine learning that achieves great power and flexibility
by learning to represent the world as nested hierarchy of concepts, with each
concept defined in relation to simpler concepts, and more abstract
representations computed in terms of less abstract ones.”
The most important
difference between deep learning and traditional machine learning is its
performance as the scale of data increases. When the data is small, deep
learning algorithms don’t perform that well.
This is because deep learning algorithms
need a large amount of data to understand it perfectly. On the other hand,
traditional machine learning algorithms with their handcrafted rules prevail in
this scenario.
Deep learning is a
subset of machine learning that differentiates itself through the way it solves
problems. Machine learning requires a domain expert to identify most applied
features. On the other hand, deep learning learns features incrementally, thus
eliminating the need for domain expertise.
This makes deep learning algorithms
take much longer to train than machine learning algorithms, which only need a
few seconds to a few hours. However, the reverse is true during testing. Deep
learning algorithms take much less time to run tests than machine learning
algorithms, whose test time increases along with the size of the data.
The key difference
between deep learning vs machine learning stems from the way data is presented
to the system. Machine learning algorithms almost always require structured
data, whereas deep learning networks rely on layers of the ANN (artificial
neural networks).
.
The biggest advantage
Deep Learning algorithms as discussed before are that they try to learn
high-level features from data in an incremental manner. This eliminates the
need of domain expertise and hard core feature extraction
The difference between
neural networks and deep learning lies in the depth of the model. Deep learning
is a phrase used for complex neural networks. ...
Deep learning is a
Neural Network consisting of a hierarchy of layers, whereby each layer
transforms the input data into more abstract representations
Deep-learning networks
are distinguished from the more commonplace single-hidden-layer neural networks
by their depth; that is, the number of node layers through which data must pass
in a multistep process of pattern recognition.
Earlier versions of
neural networks such as the first perceptrons were shallow, composed of one
input and one output layer, and at most one hidden layer in between. More than
three layers (including input and output) qualifies as “deep” learning. So deep
is not just a buzzword -- It is a strictly defined term that means more than
one hidden layer.
Deep Learning is a
subfield of machine learning concerned with algorithms inspired by the
structure and function of the brain called artificial neural networks.
In addition to
scalability, another often cited benefit of deep learning models is their
ability to perform automatic feature extraction from raw data, also called
feature learning.
Deep learning
algorithms seek to exploit the unknown structure in the input distribution in
order to discover good representations, often at multiple levels, with
higher-level learned features defined in terms of lower-level features
Deep learning methods
aim at learning feature hierarchies with features from higher levels of the
hierarchy formed by the composition of lower level features. Automatically
learning features at multiple levels of abstraction allow a system to learn
complex functions mapping the input to the output directly from data, without
depending completely on human-crafted features.
Deep learning allows
computational models that are composed of multiple processing layers to learn
representations of data with multiple levels of abstraction.
Neural networks are a
set of algorithms, modeled loosely after the human brain, that are designed to
recognize patterns. They interpret sensory data through a kind of machine
perception, labeling or clustering raw input. The patterns they recognize are
numerical, contained in vectors, into which all real-world data, be it images,
sound, text or time series, must be translated.
In deep-learning
networks, each layer of nodes trains on a distinct set of features based on the
previous layer’s output. The further you advance into the neural net, the more
complex the features your nodes can recognize, since they aggregate and
recombine features from the previous layer.
Instances where deep
learning becomes preferable include situations where there is a large amount of
data, a lack of domain understanding for feature introspection or complex
problems, such as speech recognition and natural language processing.
Deep learning and
neural networks are picking up steam in applications like self-driving cars,
radiology image processing, supply chain monitoring and cybersecurity threat
detection.
The difference between
neural networks and deep learning lies in the depth of the model. ... The
architecture has become more complex but the concept of deep learning is still
the same. Albeit there's now an increased number of hidden layers and nodes that
integrate to estimate the output(s)
Generative adversarial
networks (GANs) are deep neural net architectures comprised of two nets,
pitting one against the other (thus the “adversarial”). Generative adversarial
networks, or GANs, are a powerful type of neural network used for unsupervised
machine learning.
Made up of two
competing models which run in competition with one another, GANs are able to
capture and copy variations within a dataset
Generative artificial intelligence and deep machine learning are set to
build on the progress that basic AI has already made across business and
personal computing and daily life
Generative modeling is
an unsupervised learning task in machine learning that involves automatically
discovering and learning the regularities or patterns in input data in such a
way that the model can be used to generate or output new examples that
plausibly could have been drawn from the original dataset.
Generative Adversarial
Networks – describe pairs of alternately trained models using competing deep
learning algorithms. Here, the first model is trained using a second model, to
discriminate between actual data and synthetic data. This ability to capture
and copy variations within a dataset can be applied for uses such as
understanding risk and recovery in healthcare and pharmacology.
Deep learning
architectures such as deep neural networks, deep belief networks, recurrent
neural networks and convolutional neural networks have been applied to fields
including computer vision, speech recognition, natural language processing,
audio recognition, social network filtering, machine translation,
bioinformatics, drug design, medical image analysis, material inspection and
board game programs, where they have produced results comparable to human
experts.
Deep is a technical
term. It refers to the number of layers in a neural network. A shallow network
has one so-called hidden layer, and a deep network has more than one. Multiple
hidden layers allow deep neural networks to learn features of the data in a
so-called feature hierarchy, because simple features (e.g. two pixels)
recombine from one layer to the next, to form more complex features (e.g. a
line).
Nets with many layers pass input data (features) through more mathematical
operations than nets with few layers, and are therefore more computationally
intensive to train. Computational intensivity is one of the hallmarks of deep
learning, and it is one reason why a new kind of chip call GPUs are in demand
to train deep-learning models.
A deep neural network
(DNN) is an artificial neural network (ANN) with multiple hidden layers of
units between the input and output layers.
In exchange for large
quantities of data and processing power, deep neural networks have yielded
models that provide state of the art predication capabilities in many fields.
However, a lack of strong guarantees on their behaviour have raised concerns
over their use in safety-critical applications
To achieve an
acceptable level of accuracy, deep learning programs require access to immense
amounts of training data and processing power, neither of which were easily
available to programmers until the era of big data and cloud computing.
Because deep learning
programming can create complex statistical models directly from its own
iterative output, it is able to create accurate predictive models from large
quantities of unlabeled, unstructured data. This is important as the internet
of things (IoT) continues to become more pervasive, because most of the data
humans and machines create is unstructured and is not labeled.
Neural networks come in
several different forms, including recurrent neural networks, convolutional
neural networks, artificial neural networks and feedforward neural networks --
and each has benefits for specific use cases. However, they all function in somewhat
similar ways, by feeding data in and letting the model figure out for itself
whether it has made the right interpretation or decision about a given data
element.
Neural networks involve
a trial-and-error process, so they need massive amounts of data on which to
train. It's no coincidence neural networks became popular only after most
enterprises embraced big data analytics and accumulated large stores of data.
Because the model's first few iterations involve somewhat-educated guesses on
the contents of an image or parts of speech, the data used during the training
stage must be labeled so the model can see if its guess was accurate.
This
means, though many enterprises that use big data have large amounts of data,
unstructured data is less helpful. Unstructured data can only be analyzed by a
deep learning model once it has been trained and reaches an acceptable level of
accuracy, but deep learning models can't train on unstructured data.
Various different
methods can be used to create strong deep learning models. These techniques
include learning rate decay, transfer learning, training from scratch and
dropout.---
Learning rate decay.
The learning rate is a hyperparameter -- a factor that defines the system or
sets conditions for its operation prior to the learning process -- that
controls how much change the model experiences in response to the estimated
error every time the model weights are altered. Learning rates that are too
high may result in unstable training processes or the learning of a suboptimal
set of weights. Learning rates that are too small may produce a lengthy
training process that has the potential to get stuck.
The learning rate decay
method -- also called learning rate annealing or adaptive learning rates -- is
the process of adapting the learning rate to increase performance and reduce
training time. The easiest and most common adaptations of learning rate during
training include techniques to reduce the learning rate over time.
Transfer learning. This
process involves perfecting a previously trained model; it requires an
interface to the internals of a preexisting network. First, users feed the
existing network new data containing previously unknown classifications. Once
adjustments are made to the network, new tasks can be performed with more
specific categorizing abilities. This method has the advantage of requiring
much less data than others, thus reducing computation time to minutes or hours.
Training from scratch.
This method requires a developer to collect a large labeled data set and
configure a network architecture that can learn the features and model. This
technique is especially useful for new applications, as well as applications
with a large number of output categories. However, overall, it is a less common
approach, as it requires inordinate amounts of data, causing training to take
days or weeks.
Dropout. This method
attempts to solve the problem of overfitting in networks with large amounts of
parameters by randomly dropping units and their connections from the neural
network during training. It has been proven that the dropout method can improve
the performance of neural networks on supervised learning tasks in areas such
as speech recognition, document classification and computational biology.
Use cases today for
deep learning include all types of big data analytics applications, especially
those focused on natural language processing, language translation, medical
diagnosis, stock market trading signals, network security and image
recognition.
Specific fields in
which deep learning is currently being used include the following:----
Customer experience.
Deep learning models are already being used for chatbots. And, as it continues
to mature, deep learning is expected to be implemented in various businesses to
improve the customer experiences and increase customer satisfaction.
Text generation.
Machines are being taught the grammar and style of a piece of text and are then
using this model to automatically create a completely new text matching the
proper spelling, grammar and style of the original text.
Aerospace and military.
Deep learning is being used to detect objects from satellites that identify
areas of interest, as well as safe or unsafe zones for troops.
Industrial automation.
Deep learning is improving worker safety in environments like factories and
warehouses by providing services that automatically detect when a worker or
object is getting too close to a machine.
Adding color. Color can
be added to black and white photos and videos using deep learning models. In
the past, this was an extremely time-consuming, manual process.
Medical research.
Cancer researchers have started implementing deep learning into their practice
as a way to automatically detect cancer cells.
Computer vision. Deep
learning has greatly enhanced computer vision, providing computers with extreme
accuracy for object detection and image classification, restoration and
segmentation.
Limitations and
challenges--
The issue of biases is
a major problem for deep learning models.
This bias I call ( BIAS2 ) has nothing to do with bias related to
variance . I will go into great detail later..
If a model trains on
data that contains biases, the model will reproduce those biases in its
predictions. This has been a vexing problem for deep learning programmers,
because models learn to differentiate based on subtle variations in data
elements.
Often, the factors it
determines are important are not made explicitly clear to the programmer. This
means, for example, a facial recognition model might make determinations about
people's characteristics based on things like race or gender without the
programmer being aware.
The learning rate can
also become a major challenge to deep learning models. If the rate is too high,
then the model will converge too quickly, producing a less-than-optimal
solution. If the rate is too low, then the process may get stuck, and it will
be even harder to reach a solution.
The hardware
requirements for deep learning models can also create limitations. Multicore
high-performing graphics processing units (GPUs) and other similar processing
units are required to ensure improved efficiency and decreased time
consumption.
However, these units are expensive and use large amounts of
energy. Other hardware requirements include random access memory (RAM) and a
hard drive or RAM-based solid-state drive (SSD).
Deep reinforcement
learning has emerged as a way to integrate AI with complex applications, such
as robotics, video games and self-driving cars. The primary difference between
deep learning and reinforcement learning is, while deep learning learns from a
training set and then applies what is learned to a new data set, deep
reinforcement learning learns dynamically by adjusting actions using continuous
feedback in order to optimize the reward.
A reinforcement
learning agent has the ability to provide fast and strong control of generative
adversarial networks (GANs). The Adversarial Threshold Neural Computer (ATNC)
combines deep reinforcement learning with GANs in order to design small organic
molecules with a specific, desired set of pharmacological properties.
GANs are also being
used to generate artificial training data for machine learning tasks, which can
be used in situations with imbalanced data sets or when data contains sensitive
information.
Generative adversarial
networks (GANs) are deep neural net architectures comprised of two nets,
pitting one against the other (thus the “adversarial”). Generative adversarial
networks, or GANs, are a powerful type of neural network used for unsupervised
machine learning.
Made up of two
competing models which run in competition with one another, GANs are able to
capture and copy variations within a dataset
Generative artificial intelligence and deep machine learning are set to
build on the progress that basic AI has already made across business and
personal computing and daily life
Generative adversarial
networks (GANs) are deep neural net architectures comprised of two nets,
pitting one against the other (thus the “adversarial”). Generative adversarial
networks, or GANs, are a powerful type of neural network used for unsupervised
machine learning.
Made up of two competing models which run in competition with
one another, GANs are able to capture and copy variations within a dataset Generative artificial intelligence and deep
machine learning are set to build on the progress that basic AI has already
made across business and personal computing and daily life
Generative modeling is
an unsupervised learning task in machine learning that involves automatically
discovering and learning the regularities or patterns in input data in such a
way that the model can be used to generate or output new examples that plausibly
could have been drawn from the original dataset.
GANs are a clever way
of training a generative model by framing the problem as a supervised learning
problem with two sub-models: the generator model that we train to generate new
examples, and the discriminator model that tries to classify examples as either
real (from the domain) or fake (generated). The two models are trained together
in a zero-sum game, adversarial, until the discriminator model is fooled about
half the time, meaning the generator model is generating plausible examples.
Unlike machine
learning, deep learning uses multiple layers and structures algorithms such
that an artificial neural network is created that learns and makes decisions on
its own!
.
Human brains are made
up of connected networks of neurons. ANNs seek to simulate these networks and
get computers to act like interconnected brain cells, so that they can learn
and make decisions in a more humanlike manner.
.
ANN that is made up of
more than three layers – i.e. an input layer, an output layer and multiple
hidden layers – is called a ‘deep neural network’, and this is what underpins
deep learning. A deep learning system is self-teaching, learning as it goes by
filtering information through multiple hidden layers, in a similar way to
humans.
As you can see, the two
are closely connected in that one relies on the other to function. Without
neural networks, there would be no deep learning.
Artificial Neural
Networks (ANNs) were inspired by information processing and distributed
communication nodes in biological systems. ANNs have various differences from
biological brains. Specifically, neural networks tend to be static and
symbolic, while the biological brain of most living organisms is dynamic
(plastic) and analog.
Perceptron is a single
layer neural network and a multi-layer perceptron is called Neural Networks.
Perceptron is a linear
classifier (binary). Also, it is used in supervised learning. It helps to
classify the given input data.
The single-layer
Perceptron is the simplest of the artificial neural networks (ANNs).
Deep Learning is a
collection of statistical machine learning techniques used to learn feature
hierarchies based on the concept of
artificial neural networks.”
A Deep neural network
consists of the following layers:
The Input Layer
The Hidden Layer
The Output Layer
.
The first layer is the
input layer which receives all the inputs
The last layer is the
output layer which provides the desired output
All the layers in
between these layers are called hidden layers.
There can be n number
of hidden layers and the number of hidden layers and the number of perceptrons
in each layer will entirely depend on the use-case you are trying to solve.
Deep Learning is used
in highly computational use cases such as Face Verification, self-driving cars,
and so on. Let’s understand the importance of Deep Learning by looking at a
real-world use case.
Deep Learning is based
on the functionality of a biological neuron, so let’s understand how we mimic
this functionality in the artificial neuron (also known as a perceptron):
In a biological neuron,
dendrites are used to receive inputs. These inputs are summed in the cell body
and through the Axon, it is passed on to the next neuron.
Similar to the
biological neuron, a perceptron receives multiple inputs, applies various
transformations and functions and provides an output.
The human brain
consists of multiple connected neurons called a neural network, similarly, by
combining multiple perceptrons, we’ve developed what is known as a Deep neural
network.
A perceptron is a
simple model of a biological neuron in an artificial neural network. Perceptron
is also the name of an early algorithm for supervised learning of binary
classifiers.
The most popular
artificial neural network algorithms are:---
Perceptron
Multilayer Perceptrons
(MLP)
Back-Propagation
Stochastic Gradient
Descent
Hopfield Network
Radial Basis Function
Network (RBFN)
US psychologist Frank Rosenblatt from Cornell, invented the ‘perceptron’, an algorithm that ran on specific neuron-mimicking
hardware and was capable of learning similarly to a neural network: by
strengthening or weakening the connections between neighbouring, interconnected
neurons.
The perceptron was the ancestor of artificial neural networks and deep
learning, or what we today – 60 years later – understand as the big idea behind
‘artificial intelligence’.
A perceptron is a
simple model of a biological neuron in an artificial neural network. The perceptron
is a mathematical model of a biological neuron. While in actual neurons the
dendrite receives electrical signals from the axons of other neurons, in the
perceptron these electrical signals are represented as numerical values. ... As
in biological neural networks, this output is fed to other perceptrons.
In machine learning,
the perceptron is an algorithm for supervised learning of binary classifiers. A
binary classifier is a function which can decide whether or not an input,
represented by a vector of numbers, belongs to some specific class. It is a type of linear classifier, i.e. a
classification algorithm that makes its predictions based on a linear predictor
function combining a set of weights with the feature vector.
Rosenblatt’s pioneering
invention provided an alternative approach, one that enabled computers to go
beyond logic, and venture into solving a really hard problem: perception.
Perceptron is a single
layer neural network and a multi-layer perceptron is called Neural Networks.
Perceptron is a linear classifier (binary). Also, it is used in supervised
learning. It helps to classify the given input data.
Backpropagation is a
supervised learning algorithm, for training Multi-layer Perceptrons (Artificial
Neural Networks). The Backpropagation algorithm looks for the minimum value of
the error function in weight space using a technique called the delta rule or
gradient descent. The weights that minimize the error function is then
considered to be a solution to the learning problem.
A Perceptron is a
single layer neural network that is used to classify linear data. The
perceptron has 4 important components:---
Input
Weights and Bias
Summation Function
Activation or
transformation Function
Perceptron helps to classify the given input data.. In
machine learning, the perceptron is an algorithm for supervised learning of
binary classifiers. ... It is a type of linear classifier, i.e. a
classification algorithm that makes its predictions based on a linear predictor
function combining a set of weights with the feature vector.
One possible strategy is to use a local greedy
algorithm which works by computing the error of the perceptron for a given
weight vector, looking then for a direction in weight space in which to move,
and updating the weight vector by selecting new weights in the selected search
direction. The purpose of the learning rule is to train the network to perform
some task. ...
The learning rule is
then used to adjust the weights and biases of the network in order to move the
network outputs closer to the targets. The perceptron learning rule falls in
this supervised learning category
A single perceptron can
only be used to implement linearly separable functions. It takes both real and
boolean inputs and associates a set of weights to them, along with a bias We learn the weights, we get the function.
Let's use a perceptron to learn an OR function.
The Perceptron is inspired by the information
processing of a single neural cell called a neuron. A neuron accepts input
signals via its dendrites, which pass the electrical signal down to the cell
body. .
If the decision
boundary is non-linear, you really can’t use the Perceptron The perceptron is
the basic unit powering what is today known as deep learning. It is the
artificial neuron that, when put together with many others like it, can solve
complex, undefined problems much like humans do.
Understanding the mechanics of
the perceptron (working on its own) and multilayer perceptrons (working
together) will give you an important foundation for understanding and working
with modern neural networks. A perceptron helps to divide a set of input
signals into two parts—“yes” and “no”.
But unlike many other classification
algorithms, the perceptron was modeled after the essential unit of the human
brain—the neuron and has an uncanny ability to learn and solve complex
problems.
A perceptron is a very
simple learning machine. It can take in a few inputs, each of which has a
weight to signify how important it is, and generate an output decision of “0”
or “1”. However, when combined with many other perceptrons, it forms an
artificial neural network. A neural network can, theoretically, answer any
question, given enough training data and computing power.
What Is a Multilayer
Perceptron?
A multilayer perceptron
(MLP) is a perceptron that teams up with additional perceptrons, stacked in
several layers, to solve complex problems.
Each perceptron in the first layer on the left
(the input layer), sends outputs to all the perceptrons in the second layer
(the hidden layer), and all perceptrons in the second layer send outputs to the
final layer on the right (the output layer).. sends multiple signals, one
signal going to each perceptron in the next layer. For each signal, the
perceptron uses different weights.
Every line going from a
perceptron in one layer to the next layer represents a different output. Each
layer can have a large number of perceptrons, and there can be multiple layers,
so the multilayer perceptron can quickly become a very complex system. The
multilayer perceptron has another, more common name—a neural network.
A three-layer MLP, is
called a Non-Deep or Shallow Neural Network. An MLP with four or more layers is
called a Deep Neural Network.
One difference between
an MLP and a neural network is that in the classic perceptron, the decision
function is a step function and the output is binary.
In neural networks that
evolved from MLPs, other activation functions can be used which result in
outputs of real values, usually between 0 and 1 or between -1 and 1. This
allows for probability-based predictions or classification of items into
multiple labels.
A convolutional neural
network (CNN) is a specific type of artificial neural network that uses
perceptrons, a machine learning unit algorithm, for supervised learning, to
analyze data. CNNs apply to image processing, natural language processing and
other kinds of cognitive tasks.
Convolutional neural
networks (CNN) are one of the most popular models used today. This neural
network computational model uses a variation of multilayer perceptrons and
contains one or more convolutional layers that can be either entirely connected
or pooled.
These convolutional layers create feature maps that record a region
of image which is ultimately broken into rectangles and sent out for nonlinear
processing.
The CNN model is particularly popular in the realm of image
recognition; it has been used in many of the most advanced applications of AI,
including facial recognition, text digitization and natural language
processing. Other uses include paraphrase detection, signal processing and
image classification.
Deconvolutional neural
networks utilize a reversed CNN model process. They aim to find lost features
or signals that may have originally been considered unimportant to the CNN
system's task. This network model can be used in image synthesis and analysis.
Modeled in accordance
with the human brain, a Neural Network was built to mimic the functionality of
a human brain. The human brain is a neural network made up of multiple neurons,
similarly, an Artificial Neural Network (ANN) is made up of multiple
perceptrons
A neural network
consists of three important layers:--
Input Layer: As the
name suggests, this layer accepts all the inputs provided by the programmer.
Hidden Layer: Between
the input and the output layer is a set of layers known as Hidden layers. In
this layer, computations are performed which result in the output.
Output Layer: The
inputs go through a series of transformations via the hidden layer which
finally results in the output that is delivered via this layer.
Neural networks can be
hardware- (neurons are represented by physical components) or software-based
(computer models), and can use a variety of topologies and learning algorithms.
To understand neural
networks, we need to break it down and understand the most basic unit of a
Neural Network, i.e. a Perceptron.
Like Logistic
Regression, the Perceptron is a linear classifier used for binary predictions.
This means that in order for it to work, the data must be linearly separable.
Although the Perceptron
is only applicable to linearly separable data, the more detailed Multilayered
Perceptron can be applied to more complicated nonlinear datasets. This includes
applications in areas such as speech recognition, image processing, and
financial predictions just to name a few.
Neural networks are a
specific set of algorithms that have revolutionized machine learning. They are
inspired by biological neural networks and the current so-called deep neural
networks have proven to work quite well.
Neural networks, a
beautiful biologically-inspired programming paradigm which enables a computer
to learn from observational data. Deep
learning, a powerful set of techniques for learning in neural networks.
Deep learning is the
reigning monarch of AI. In the six years since it exploded into the mainstream,
it has become the dominant way to help machines sense and perceive the world
around them.
Deep Learning is a
machine learning technique that uses structures called “neural networks” that
are inspired by the human brain. These
consist of a set of layered units, modeled after neurons. Each layer of units
processes a set of input values and produces output values that are passed onto
the next layer of units.
Neural networks often
consist of more than 100 layers, with a large number of units in each layer to
enable the recognition of extremely complex and precise patterns in data.
To explore this
further, consider image recognition software that utilizes neural networks to
identify a picture of an elephant. The
first layer of units might look at the raw image data for the most basic
patterns, perhaps that there is a thing
with what appears to be four legs. Then the next layer would look for patterns
within these patterns, perhaps that it
is an animal. Then maybe the next layer identifies the trunk.
This process
would continue throughout many layers,
recognizing increasingly precise patterns in the image, until the network
is able to identify that it is indeed a
picture of an elephant.
Progress in deep
learning has been the cause for much of the optimism about AI due its ability
to process and find patterns in massive
amounts of data with accuracy Whereas
early ML typically uses a decisiontree structure, deep learning has become the
dominant technique. It is often used to power specific ML approaches such as machine vision and natural
language processing.
Neural Networks are a
class of models within the general machine learning literature. Neural networks
are a specific set of algorithms that have revolutionized machine learning.
They are inspired by biological neural networks and the current so-called deep
neural networks have proven to work quite well.
The term “Neural Net” refers to
both the biological and artificial variants, although typically the term is
used to refer to artificial systems only.
Mathematically, neural
nets are nonlinear Artificial neural networks are parallel computational models
(unlike our computers, which have a single processor to collect and display
information). ... The basis for these networks originated from the biological
neuron and neural structures – every neuron takes in multiple unique inputs and
produces one output.
Neural Networks are a
class of models within the general machine learning literature. Neural networks
are a specific set of algorithms that have revolutionized machine learning. They
are inspired by biological neural networks and the current so-called deep
neural networks have proven to work quite well.
Neural Networks are
themselves general function approximations, which is why they can be applied to
almost any machine learning problem about learning a complex mapping from the
input to the output space.
Deep learning is a
powerful tool to make prediction an actionable result. Deep learning excels in
pattern discovery (unsupervised learning) and knowledge-based prediction. Big
data is the fuel for deep learning. When both are combined, an organization can
reap unprecedented results in term of productivity, sales, management, and
innovation.
Deep learning can
outperform traditional method. For instance, deep learning algorithms are 41%
more accurate than machine learning algorithm in image classification, 27 %
more accurate in facial recognition and 25% in voice recognition...
There is a category of
AI algorithms that are both a part of ML and AI but are more specialized than
machine learning algorithms. These are known as deep learning algorithms, and
exhibit characteristics of machine learning while being more advanced.
In the human brain, any
cognitive processes are conducted by small cells known as neurons communicating
with each other. The entire brain is made up of these neurons, which form a
complex network that dictates our actions as humans. This is what deep learning
algorithms aim to recreate.
They are created with
the help of digital constructs known as neural networks, which directly mimic
the physical structure of the human brain in order to solve problems. While
explainable AI had already been a problem with machine learning, explaining the
actions of deep learning algorithms is considered nearly impossible today.
Deep learning
algorithms may hold the key to more powerful AI, as they can perform more
complex tasks than machine learning algorithms can. It learns from itself as
more data is fed to it, like machine learning algorithms. However, deep
learning algorithms function differently when it comes to gathering information
from data.
Deep learning uses the concept of neural networks to solve
complex problems. It is mainly used to deal with high dimensional
data. It is based o the concept of Neural Networks and is often used in object
detection and image processing.
AI, Machine Learning
and Deep Learning are interconnected fields. Machine Learning and Deep learning
aids Artificial Intelligence by providing a set of algorithms and neural
networks to solve data-driven problems.
Artificial Intelligence
is not restricted to only Machine learning and Deep learning. It covers a vast
domain of fields including, Natural Language Processing (NLP), object
detection, computer vision, robotics, expert systems and so on.
Deep learning
algorithms run data through several “layers” of neural network algorithms, each
of which passes a simplified representation of the data to the next layer. Most
machine learning algorithms work well on datasets that have up to a few hundred
features, or columns.
Deep learning
algorithms use multiple layers to progressively extract higher level features
from raw input. For example, in image processing, lower layers may identify
edges, while higher layers may identify human-meaningful items such as digits
or letters or faces.
Neural networks are a
set of algorithms, modeled loosely after the human brain, that are designed to
recognize patterns. They interpret sensory data through a kind of machine
perception, labeling or clustering raw input. The patterns they recognize are
numerical, contained in vectors, into which all real-world data, be it images,
sound, text or time series, must be translated.
Neural networks help us
cluster and classify. You can think of them as a clustering and classification
layer on top of the data you store and manage. They help to group unlabeled
data according to similarities among the example inputs, and they classify data
when they have a labeled dataset to train on.
Neural networks can also
extract features that are fed to other algorithms for clustering and
classification; so you can think of deep neural networks as components of
larger machine-learning applications involving algorithms for reinforcement
learning, classification and regression.
The following are the
limitations of Machine Learning:----
Machine Learning is not
capable of handling and processing high dimensional data.
It cannot be used in
image recognition and object detection since they require the implementation of
high dimensional data
Another major challenge
in Machine Learning is to tell the machine what are the important features it
should look for in order to precisely predict the outcome. This very process is
called feature extraction. Feature extraction is a manual process in Machine
Learning.
The above limitations
can be solved by using Deep Learning.
Deep learning is one of
the methods by which we can overcome the challenges of feature extraction. This
is because deep learning models are capable of learning to focus on the right
features by themselves, requiring minimal human interventions.
Deep Learning mimics
the basic component of the human brain called a brain cell or a neuron. Inspired
from a neuron an artificial neuron was developed..
PayPal is no stranger
to fraud. As one of the Internet’s first online payment services, PayPal has
been exposed to every type of wire fraud imaginable (and some beyond
imagination).
Sometimes the fraudsters had the upper hand, but now, thanks to
deep learning (DL) models running on high performance computing (HPC)
infrastructure, PayPal is leveraging its vast repository of fraud data to keep
the fraudsters on the run.
Consider how PayPal
uses Deep Learning to identify any possible fraudulent activities. PayPal
processed over $235 billion in payments from four billion transactions by its
more than 170 million customers.
Considering that the
company processed $235 billion in transactions for its 170 billion customers last year, the
$300 million that it spent fighting fraud would seem to be a good investment.
PayPal used Machine
learning and Deep Learning algorithms to mine data from the customer’s
purchasing history in addition to reviewing patterns of likely fraud stored in
its databases to predict whether a particular transaction is fraudulent or not.
Running a payment
processing system on the Internet is no easy business, and PayPal faces substantial
financial and reputational risks from hackers, conmen, organized crime, and
money launderers alike. In fact, the company struggled mightily with fraud in
its early days, and incurred heavy losses.
According to the 2006 book “The
PayPal Wars,” PayPal once had fraud losses amounting to 120 basis points (or
1.2% of the value of transactions), and lost $2,300 per hour to fraud.
PayPal stepped up its
fraud-fighting game by using more advanced technologies, such as neural
networks and Gradient Boosted Trees (GBTs).
The new deep learning
models are voracious consumers of data.
PayPal has one big
advantage over the fraudsters: a huge repository of data. This data repository
was important for training classical machine learning models, but it’s
absolutely critical for training a deep learning model.
The new deep learning
approach lets PayPal vary the types of data that it’s using to predict which
users, transactions, or locations are likely to be fraudulent, or associated
with fraud.
Deep learning is also
bolstering PayPal’s ability to utilize technology developed in other domains
and transfer it to the fraud-detection business
AI is an algorithm that
allows a system to learn from its environment and available inputs. In advanced
applications, such as self-driving cars, AI is trained using an approach called
deep learning, which relies solely on large volumes of sensor data without
human involvement. However, these machine learning systems can fail when the
training data misleads the decision making process.
For many applications,
an AI failure is merely an inconvenience as long as the AI works as expected
most of the time. However, for applications, such as self-driving cars and
medical diagnosis in which failure can result in death or catastrophe, repeated
unexpected failures are not tolerated.
Deep learning is a
subset of machine learning where artificial neural networks, algorithms
inspired by the human brain, learn from large amounts of data. Similarly to how
we learn from experience, the deep learning algorithm would perform a task
repeatedly, each time tweaking it a little to improve the outcome.
The neural
networks have various (deep) layers that enable learning. Just about any problem that requires
“thought” to figure out is a problem deep learning can learn to solve.
DL is a set of
algorithms in machine learning that attempt to learn in multiple levels, where
the lower-level concepts help define different higher-level concepts.
The most important
difference between deep learning and traditional machine learning is its
performance as the scale of data increases. When the data is small, deep
learning algorithms don't perform that well. This is because deep learning
algorithms need a large amount of data to understand it perfectly
The key difference
between deep learning vs machine learning stems from the way data is presented
to the system. Machine learning algorithms almost always require structured
data, whereas deep learning networks rely on layers of the ANN (artificial
neural networks)
ANN takes data samples
rather than entire data sets to arrive at solutions
There are multiple
types of neural network, each of which come with their own specific use cases
and levels of complexity. The most basic type of neural net is something called
a feedforward neural network, in which information travels in only one direction
from input to output. A feedforward neural network is an
artificial neural network wherein connections between the nodes do not form a
cycle
A more widely used type
of network is the recurrent neural network, in which data can flow in multiple
directions. These neural networks possess greater learning abilities. A
recurrent neural network (RNN) is a class of artificial neural networks where
connections between nodes form a directed graph along a temporal sequence. This
allows it to exhibit temporal dynamic behavior.
Unlike feedforward neural
networks, RNNs can use their internal state (memory) to process sequences of
inputs. This makes them applicable to tasks such as unsegmented, connected
handwriting recognition or speech recognition
There are also
convolutional neural networks, Boltzmann machine networks, and a variety of others. Picking the right
network for your task depends on the data you have to train it with, and the specific
application you have in mind. In some cases, it may be desirable to use
multiple approaches, such as would be the case with a challenging task like
voice recognition.
A Boltzmann Machine is
a network of symmetrically connected, neuron- like units that make stochastic
decisions about whether to be on or off. Boltz- mann machines have a simple
learning algorithm that allows them to discover interesting features in
datasets composed of binary vectors
There were a few
drawbacks in Machine learning that led to the emergence of Deep Learning..
Deep learning is
self-education for machines; you feed an AI huge amounts of data, and
eventually it begins to discern patterns all by itself
Deep learning, and most
machine learning (ML) methods for that matter, learn patterns or associations
from data. On its own, observational data can only possibly convey associations
between variables -- the familiar adage correlation does not imply causation.
Machines don’t evolve
on their own. The next step in deep learning will not come from a machine
iterating on itself. It will come when humans iterate on their previous work.
For now, we are still the agents hiding in the apparatus. We still make the
moves.
Current approaches to
NLP are based on deep learning, a type of AI that examines and uses patterns in
data to improve a program's understanding. Deep learning models require massive
amounts of labeled data to train on and identify relevant correlations, and
assembling this kind of big data set is one of the main hurdles to NLP
currently.
Deep learning doesn’t
understand human language. Period!
Deep learning allows
machines to solve complex problems even when using a data set that is very
diverse, unstructured and inter-connected. The more deep learning algorithms
learn, the better they perform.
Deep learning
algorithms can automatically translate between languages. This can be powerful
for travelers, business people and those in government.
.
Transforming
black-and-white images into color was formerly a task done meticulously by
human hand. Today, deep learning algorithms are able to use the context and
objects in the images to color them to basically recreate the black-and-white
image in color. The results are impressive and accurate.
Deep learning is being
used for facial recognition .. The challenges for deep-learning algorithms for
facial recognition is knowing it’s the same person even when they have changed
hairstyles, grown or shaved off a beard or if the image taken is poor due to
bad lighting or an obstruction.
The more experience
deep-learning algorithms get, the better they become. . The benchmark for AI is
the human intelligence regarding reasoning, speech, and vision. This benchmark
is far off in the future.
Deep learning is the
breakthrough in the field of artificial intelligence. When there is enough data
to train on, deep learning achieves impressive results, especially for image
recognition and text translation. .
Deep learning uses huge
neural networks with many layers of processing units, taking advantage of
advances in computing power and improved training techniques to learn complex
patterns in large amounts of data. Common applications include image and speech
recognition.
Computer vision relies
on pattern recognition and deep learning to recognize what’s in a picture or
video. When machines can process, analyze and understand images, they can
capture images or videos in real time and interpret their surroundings.
Deep learning
architectures such as deep neural networks, deep belief networks, recurrent
neural networks and convolutional neural networks have been applied to fields
including computer vision, speech recognition, natural language processing,
audio recognition, social network filtering, machine translation,
bioinformatics, drug design, medical image analysis, material inspection and
board game programs, where they have produced results comparable to and in some
cases superior to human experts.
The "deep" in
"deep learning" refers to the number of layers through which the data
is transformed. .
Deep learning-based
image recognition has become "superhuman", producing more accurate
results than human contestants.
Deep learning has been
shown to produce competitive results in medical application such as cancer cell
classification, lesion detection, organ segmentation and image enhancement
.
The United States
Department of Defense applied deep learning to train robots in new tasks
through observation.
Cloud computing
facilitates speedy, enhanced development of advanced AI capabilities, such as
deep learning. Some current deep learning applications make security cameras
smarter by spotting patterns that could indicate trouble.
Such technology can
also categorize images. The deep learning technology inside self-driving cars
differentiates between people and road signs.
A shallow network has
one so-called hidden layer, and a deep network has more than one. Multiple
hidden layers allow deep neural networks to learn features of the data in a
so-called feature hierarchy, because simple features (e.g. two pixels)
recombine from one layer to the next, to form more complex features (e.g. a
line).
Nets with many layers pass input data (features) through more
mathematical operations than nets with few layers, and are therefore more
computationally intensive to train.
Computational intensivity is one of the
hallmarks of deep learning, and it is one reason why a new kind of chip call
GPUs are in demand to train deep-learning models.
Deep learning
algorithms must be trained on big datasets. Once trained, inference puts a
trained model to work, to draw conclusions or make predictions
GPU(Graphics Processing
Unit) is considered as heart of Deep Learning, a part of Artificial
Intelligence. It is a single chip processor used for extensive Graphical and
Mathematical computations which frees up CPU cycles for other jobs
The individual
accelerator chip is designed to perform the execution side of deep learning
rather than the training part. Engineers generally measure the performance of
such “inferencing” chips in terms of how many operations they can do per joule
of energy or millimeter of area
A GPU, or graphics
processing unit, is used primarily for 3D applications. It is a single-chip
processor that creates lighting effects and transforms objects every time a 3D
scene is redrawn. These are mathematically-intensive tasks, which otherwise,
would put quite a strain on the CPU
.
The GPU, or graphics
processing unit, is a part of the video rendering system of a computer. The
typical function of a GPU is to assist with the rendering of 3D graphics and
visual effects so that the CPU doesn't have to
Highly specialized
chips designed for a specific computational task can outperform GPU chips that
are good at handling many different kinds of computation.
Hardware design, rather
than algorithms, will help us achieve the next big breakthrough in AI.
A tensor processing
unit (TPU) is an AI accelerator application-specific integrated circuit (ASIC)
developed by Google specifically for neural network machine learning
A neural processor or a
neural processing unit (NPU) is a specializes circuit that implements all the
necessary control and arithmetic logic necessary to execute machine learning
algorithms, typically by operating on predictive models such as artificial
neural networks (ANNs) or random forests (RFs).
NPUs sometimes go by
similar names such as a tensor processing unit (TPU), neural network processor
(NNP) and intelligence processing unit (IPU) as well as vision processing unit
(VPU) and graph processing unit (GPU).
Executing deep neural
networks such as convolutional neural networks means performing a very large
amount of multiply-accumulate operations, typically in the billions and
trillions of iterations.
A large number of
iterations comes from the fact that for each given input (e.g., image), a
single convolution comprises of iterating over every channel and then every
pixel and performing a very large number of MAC operations. And many such
convolutions are found in a single model and the model itself must be executed
on each new input (e.g., every camera frame capture).
Unlike traditional
central processing units which are great at processing highly serialized
instruction streams, Machine learning workloads tend to be highly
parallelizable, much like a graphics processing unit.
Moreover, unlike a GPU,
NPUs can benefit from vastly simpler logic because their workloads tend to
exhibit high regularity in the computational patterns of deep neural networks.
For those reasons, many custom-designed dedicated neural processors have been
developed.
Tensor Processing Units
(TPUs) are Google’s custom-developed application-specific integrated circuits
(ASICs) used to accelerate machine learning workloads. Cloud TPU resources
accelerate the performance of linear algebra computation, which is used heavily
in machine learning applications.
TPUs minimize the time-to-accuracy when you
train large, complex neural network models. Models that previously took weeks to
train on other hardware platforms can converge in hours on TPUs.
AI analyzes more and
deeper data using neural networks that have many hidden layers. Building a
fraud detection system with five hidden layers was almost impossible a few
years ago. All that has changed with incredible computer power and big data.
You need lots of data to train deep learning models because they learn directly
from the data. The more data you can feed them, the more accurate they become.
Your interactions with
Alexa, Google Search and Google Photos are all based on deep learning – and
they keep getting more accurate the more we use them. In the medical field, AI
techniques from deep learning, image classification and object recognition can
now be used to find cancer on MRIs high accuracy .
Cognitive computing is
a subfield of AI that strives for a natural, human-like interaction with
machines. Using AI and cognitive computing, the ultimate goal is for a machine
to simulate human processes through the ability to interpret images and speech
– and then speak coherently in response.
Computer vision relies
on pattern recognition and deep learning to recognize what’s in a picture or
video. When machines can process, analyze and understand images, they can
capture images or videos in real time and interpret their surroundings.
Artificial intelligence
(AI) makes it possible for machines to learn from experience, adjust to new
inputs and perform human-like tasks.
Most AI examples that you hear about today – from chess-playing
computers to self-driving cars – rely heavily on deep learning and natural
language processing. Using these technologies, computers can be trained to
accomplish specific tasks by processing large amounts of data and recognizing
patterns in the data.
A layer is the
highest-level building block in deep learning. A layer is a container that
usually receives weighted input, transforms it with a set of mostly non-linear
functions and then passes these values as output to the next layer.
.
The input layer of a
neural network is composed of artificial input neurons, and brings the initial
data into the system for further processing by subsequent layers of artificial
neurons. The input layer is the very beginning of the workflow for the
artificial neural network.
The most popular deep
learning algorithms are:---
Convolutional Neural
Network (CNN)
Recurrent Neural
Networks (RNNs)
Long Short-Term Memory
Networks (LSTMs)
Stacked Auto-Encoders
Deep Boltzmann Machine
(DBM)
Deep Belief Networks
(DBN)
There is a vast amount
of neural network, where each architecture is designed to perform a given task.
For instance, CNN works very well with pictures, RNN provides impressive
results with ti.me series and text analysis.
CNN, or convolutional
neural network, is a neural network using convolution layer and pooling layer.
The convolution layer convolves an area, or a stuck of elements in input data,
into smaller area to extract feature.. Any
data that has spatial relationships is ripe for applying CNN.
CNNs are currently the most complicated and
advanced type of neural networks and are used in various field such as
self-driving cars, medical imaging, facial recognition and speech recognition.
CNN is a multi-layered
neural network with a unique architecture designed to extract increasingly
complex features of the data at each layer to determine the output. CNN's are
well suited for perceptual tasks.
A convolution is the
simple application of a filter to an input that results in an activation.
Repeated application of the same filter to an input results in a map of
activations called a feature map, indicating the locations and strength of a
detected feature in an input, such as an image.
The innovation of
convolutional neural networks is the ability to automatically learn a large
number of filters in parallel specific to a training dataset under the
constraints of a specific predictive modeling problem, such as image
classification. The result is highly specific features that can be detected
anywhere on input images.
CNN is mostly used when
there is an unstructured data set (e.g., images) and the practitioners need to
extract information from it
Convolutional neural
network (CNN) identifies and makes sense of images. Uses for convolutional
neural networks include image classification (e.g. training machines to
distinguish between images of cats and dogs).
For instance, if the
task is to predict an image caption:-
The CNN receives an
image of let's say a cat, this image, in computer term, is a collection of the
pixel. Generally, one layer for the greyscale picture and three layers for a
color picture.
During the feature
learning (i.e., hidden layers), the network will identify unique features, for
instance, the tail of the cat, the ear, etc.
When the network
thoroughly learned how to recognize a picture, it can provide a probability for
each image it knows. The label with the highest probability will become the
prediction of the network.
Simple deep learning
techniques like CNN can, in some cases, imitate the knowledge of experts in
medicine and other fields. The current wave of machine learning, however,
requires training data sets that are not only labeled but also sufficiently
broad and universal.
A convolutional neural
network (CNN) is used in image recognition and processing that is specifically
designed to process pixel data.
Convolutional Neural
Networks have a different architecture than regular Neural Networks. Regular
Neural Networks transform an input by putting it through a series of hidden
layers. Every layer is made up of a set of neurons, where each layer is fully connected
to all neurons in the layer before.
Convolution and the
convolutional layer are the major building blocks used in convolutional neural
networks. A convolution is the simple application of a filter to an input that
results in an activation. ... The result is highly specific features that can
be detected anywhere on input images
Why are convolutional
neural networks better than other neural networks in processing data such as
images and video? The reason why Convolutional Neural Networks (CNNs) do so
much better than classic neural networks on images and videos is that the
convolutional layers take advantage of inherent properties of images.
In convolutional
(filtering and encoding by transformation) neural networks (CNN) every network
layer acts as a detection filter for the presence of specific features or
patterns present in the original data.
Convolution is a way to give the network a
degree of translation invariance. You can think of the typical image convolution
used in neural networks as a form of blurring (though there are other kinds of
convolutions that are closer to spatial derivatives)
At the end of a CNN,
the output of the last Pooling Layer acts as input to the so called Fully
Connected Layer. There can be one or more of these layers (“fully connected”
means that every node in the first layer is connected to every node in the
second layer)
CNN-Cert requires full
visibility into the structure of a neural network. The method uses mathematical
techniques to define thresholds on the input-output relationships of each layer
and each neuron. This enables it to determine how changes to the input in
different ranges will affect the outputs of each unit and layer.
CNN-Cert first executes
the process on single neurons and layers and then propagates it across the
network. The final result is a threshold value that determines the amount of
perturbations the network can resist before its output values become erroneous.
CNN builds an image in
memory that incorporates aspects of all the data it's been given. So if a CNN
is taught to recognize a printed or handwritten character of text, it's because
it's seen several examples of every such character, and has built up a
"learned" image of each one that has its basic features.
If a CNN model is
trained with a variety of human faces, it will have built an amalgam of those
faces in its model -- perhaps not necessarily a photograph, but a series of
functions that represents the basic geometry of a face, such as the angle
between the tip of the ear, the top of the cheekbone, and the tip of the nose.
Train that model with a series of faces of known terrorists, and the model
should build some basic construct that answers the question, "What does a Isis Islamic terrorist look like?" There's no way a person could rationally respond to
that question in a manner that some, if not most, listeners would consider
unbiased.
And if a process were capable of rendering that average terrorist's
face in living color, someone, somewhere would be rightfully enraged.
But think of this
problem from the perspective of a software developer: Isolating individuals'
faces in a crowd from CCTV footage and comparing them point-by-point to
individual terrorists' mug shots, is a job that even a supercomputer might not
be able to perform in anything approaching real-time.
How should the developer winnow the crowd
faces to a more manageable subset? A CNN
offers one option: an "average face" capable of excluding a wide
range of improbable candidates. It's the visual equivalent of a hash function,
simplifying long searches by making quick comparisons to some common element.
CNN can perform
impressive feats of image recognition, but it is difficult to trace exactly how
they arrive at their decisions. There are many layers of classification and
comparison, and the end result is both remarkably accurate and consistent. But
that does not mean we can truly explain how the CNN arrived at its decision for
each image.
CNN can perform
impressive feats of image recognition, but it is difficult to trace exactly how
they arrive at their decisions. There are many layers of classification and
comparison, and the end result is both remarkably accurate and consistent. But
that does not mean we can truly explain how the CNN arrived at its decision for
each image.
Convolutional Neural
Networks (CNN) is one kind of feedforward neural network. ... CNN is an
efficient recognition algorithm which is widely used in pattern recognition and
image processing. It has many features such as simple structure, less training
parameters and adaptability.
Recurrent neural
networks (RNNs) is a multi-layered neural network that can store information in
context nodes, allowing it to learn data sequences and output a number or
another sequence. In simple words it an Artificial neural networks whose
connections between neurons include loops. RNNs are well suited for processing
sequences of inputs.
A recurrent neural
network (RNN) is a class of artificial neural networks where connections
between nodes form a directed graph along a temporal sequence. This allows it
to exhibit temporal dynamic behavior. Unlike feedforward neural networks, RNNs
can use their internal state (memory) to process sequences of inputs.
Recurrent Neural
Network comes into the picture when any model needs context to be able to
provide the output based on the input.
CNN is used for spatial
data and RNN is used for sequence data. Second, CNN is kind of more powerful
now than RNN. ... This is mostly because CNN can be stacked into a very deep
model, which has been proven to be very effective.
The Convolutional
Recurrent Neural Networks is the combination of two of the most prominent
neural networks. The CRNN (convolutional recurrent neural network) involves CNN
(convolutional neural network) followed by the RNN(Recurrent neural networks)..
The
convolutional layers are developed on 3-dimensional feature vectors, whereas
the recurrent neural networks are developed on 2-dimensional feature vectors.
The first layer of a
neural network will learn small details from the picture; the next layers will
combine the previous knowledge to make more complex information. In the
convolutional neural network, the feature extraction is done with the use of
the filter.
The network applies a filter to the picture to see if there is a
match, i.e., the shape of the feature is identical to a part of the image. If
there is a match, the network will use this filter. The process of feature
extraction is therefore done automatically.
Deep learning
algorithms are constructed with connected layers.
The first layer is
called the Input Layer
The last layer is
called the Output Layer
All layers in between
are called Hidden Layers. The word deep means the network join neurons in more
than two layers.
Each Hidden layer is
composed of neurons. The neurons are connected to each other. The neuron will
process and then propagate the input signal it receives the layer above it. The
strength of the signal given the neuron in the next layer depends on the
weight, bias and activation function.
The network consumes
large amounts of input data and operates them through multiple layers; the
network can learn increasingly complex features of the data at each layer.
A deep neural network
provides state-of-the-art accuracy in many tasks, from object detection to
speech recognition. They can learn automatically, without predefined knowledge
explicitly coded by the programmers.
Each layer represents a
deeper level of knowledge, i.e., the hierarchy of knowledge. A neural network
with four layers will learn more complex feature than with that with two
layers.
The learning occurs in
two phases.
The first phase
consists of applying a nonlinear transformation of the input and create a
statistical model as output.
The second phase aims
at improving the model with a mathematical method known as derivative.
The neural network
repeats these two phases hundreds to thousands of time until it has reached a
tolerable level of accuracy. The repeat of this two-phase is called an
iteration.
Why are convolutional
neural networks better than other neural networks in processing data such as
images and video? The reason why Convolutional Neural Networks (CNNs) do so
much better than classic neural networks on images and videos is that the
convolutional layers take advantage of inherent properties of images.
Deep artificial neural
networks are a set of algorithms that have set new records in accuracy for many
important problems, such as image recognition, sound recognition, recommender
systems, natural language processing etc.
Neural networks help us
cluster and classify. You can think of them as a clustering and classification
layer on top of the data you store and manage. They help to group unlabeled
data according to similarities among the example inputs, and they classify data
when they have a labeled dataset to train on.
Neural networks can
also extract features that are fed to other algorithms for clustering and
classification; so you can think of deep neural networks as components of
larger machine-learning applications involving algorithms for reinforcement
learning, classification and regression.
Neural networks
generally perform supervised learning tasks, building knowledge from data sets
where the right answer is provided in advance. The networks then learn by
tuning themselves to find the right answer on their own, increasing the
accuracy of their predictions
Deep Learning. systems
specifically rely upon non-linear neural networks to build out machine learning
systems, often relying upon using the machine learning to actually model the
system doing the modeling. It is mostly a subset of machine learning with a
specific emphasis on neural nets.
Again, deep neural
network is learning architecture based on data representations. They’re loosely
modeled on the brain . DNNs comprise artificial neurons (i.e., mathematical
functions) connected with synapses that transmit signals to other neurons.
Said neurons are
arranged in layers, and those signals — the product of data, or inputs, fed
into the DNN — travel from layer to layer and slowly “tune” the DNN by
adjusting the synaptic strength — weights — of each neural connection.
Over time, after
hundreds or even millions of cycles, the network extracts features from the
dataset and identifies trends across samples, eventually learning to make novel
predictions.
Technologies including
machine learning, neural networks, and deep learning now help data scientists
face the challenges raised by the need for prediction.
Basically, an
artificial neural network consists of an input layer where it receives inputs
and output layer where it outputs. The hidden layer is a layer which is hidden
in between input and output layers since the output of one layer is the input
of another layer
The number of hidden
neurons should be between the size of the input layer and the size of the
output layer. The number of hidden neurons should be 2/3 the size of the input
layer, plus the size of the output layer. The number of hidden neurons should
be less than twice the size of the input layer.
Deep NN is just a deep
neural network, with a lot of layers.
Today, neural networks
are used for solving many business problems such as sales forecasting, customer
research, data validation, and risk management. For example, at Statsbot they
apply neural networks for time series predictions, anomaly detection in data,
and natural language understanding
Classification of
Neural Networks---
Shallow neural network:
The Shallow neural network has only one hidden layer between the input and
output.
Deep neural network:
Deep neural networks have more than one layer. For instance, Google LeNet model
for image recognition counts 22 layers.
Nowadays, deep learning
is used in many ways like a driverless car, mobile phone, Google Search Engine,
Fraud detection, TV, and so on.
Feed-forward neural
networks is the simplest type of
artificial neural network. With this type of architecture, information flows in
only one direction, forward. It means, the information's flows starts at the
input layer, goes to the "hidden" layers, and end at the output
layer.
The
network does not have a loop.
Information stops at the output layers.
A deep neural network
is a pipeline of operations that processes data to identify some pattern as a
solution to a specific problem. Deep neural nets have a layered structure, and
the ‘thinking’ part happens inside the hidden layers between the input and
output.
The ‘deep’ indicates that there are several internal layers, so the
incoming data goes through more complex transformations, as opposed to simpler
artificial neural networks.
There are many
applications of deep neural networks, and a very basic example is image
recognition. In this case, the neural net would take an image as an input and
would try to guess with some probability what objects are on it.
However, to make any
neural network good in its job, it first has to be trained. In other words, the
algorithm has to fine-tune its inner workings, i.e. the parameters that assign
values of connections between its neurons. Training requires the neural network
to be fed with data and then, as it returns some output, to have its parameters
corrected in a way that would make its next prediction better.
So usually a deep
neural network would initially spit some nonsense result, that is very far from
the truth. Typically, a human researcher will try to adjust the architecture of
the algorithm by adding a layer, changing the type of another, etc.
This
process goes on until we’re happy with the AI accuracy.
So adjusting the
architecture is the only part that requires human intervention. But it usually
is a very tedious and time-consuming process. A hit-and-miss, so to say.
Neural Architecture
Search (NAS) utilizes an algorithm called a controller (most often a neural
network). It creates a child neural network and proceeds to optimize its
architecture until it finds the best solution for a particular problem, i.e.
until the child network achieves its highest accuracy. A well-working NAS eliminates the need of humans from the process of deep neural
network creation.
Neural architecture
search (NAS) is a technique for automating the design of artificial neural
networks (ANN), a widely used model in the field of machine learning. NAS has
been used to design networks that are on par or outperform hand-designed
architectures
Deep learning is a
computer software that mimics the network of neurons in a brain. It is a subset
of machine learning and is called deep learning because it makes use of deep
neural networks. The machine uses different layers to learn from the data. The
depth of the model is represented by the number of layers in the model.
Deep
learning is the new state of the art in term of AI. In deep learning, the
learning phase is done through a neural network. A neural network is an
architecture where the layers are stacked on top of each other
In deep learning, the
learning phase is done through a neural network.. A
neural network is an architecture where the layers are stacked on top of each
other.
Artificial neural
networks are parallel computational models (unlike our computers, which have a
single processor to collect and display information). ... The basis for these
networks originated from the biological neuron and neural structures – every
neuron takes in multiple unique inputs and produces one output.
Deep learning, uses
advanced computing power and special types of neural networks and applies them
to large amounts of data to learn, understand, and identify complicated
patterns. Automatic language translation and medical diagnoses are examples of
deep learning.
The key difference
between deep learning vs machine learning stems from the way data is presented
to the system. Machine learning algorithms almost always require structured
data, whereas deep learning networks rely on layers of the ANN (artificial
neural networks)
A neural network may
only have a single layer of data, while a deep neural network has two or more
In deep learning, the
learning phase is done through a neural network. A neural network is an
architecture where the layers are stacked on top of each other. The training set
would be fed to a neural network
Each input goes into a
neuron and is multiplied by a weight. The result of the multiplication flows to
the next layer and become the input. This process is repeated for each layer of
the network. The final layer is named the output layer; it provides an actual
value for the regression task and a probability of each class for the
classification task.
The neural network uses a mathematical algorithm to update
the weights of all the neurons. The neural network is fully trained when the
value of the weights gives an output close to the reality. For instance, a
well-trained neural network can recognize the object on a picture with higher
accuracy than the traditional neural net.
DNN architectures
enable the models to learn multiple hierarchical, hence deep, inter
relationships of the features present in the data.
Deep learning, modeled
loosely on the biological brain with layers of neural networks, tend to be
single-purpose point-solutions that require extensive training with massive
data sets, and are customized for a specific environment. In contrast,
evolutionary algorithms solves a problem based on criteria set in the “fitness”
function—thus requiring little to no data.
Within a neural
network, each processor or “neuron,” is typically activated through sensing
something about its environment, from a previously activated neuron, or by
triggering an event to impact its environment.
The goal of these activations is
to make the network—which is a group of ML algorithms—achieve a certain
outcome. Deep learning is about “accurately assigning credit across many such
stages” of activation.
.
As the name suggests,
artificial neural networks (ANNs) are inspired by the functionality of theelectro-chemical
neural networks found in human (and animal) brains. The working of the brain remains
somewhat mysterious, although it has long been known that signals from stimuli
are transmitted and altered as they pass through complex networks of neurons.
In an ANN, inputs are passed through a network, generating outputs that are
interpreted as responses.
The process starts with
signals being sent to the 'input layer', and
ends with a response being generated at the 'output layer'. In between, there is one or more
'hidden layer', which manipulates the
signal as it passes through, so that it generates a useful output. ..
The, signals then pass
to the next layer, so neurons in this
hidden layer receive several numbers. Each
neuron in this layer combines and manipulates these signals in different ways to generate a
single numerical output. For example,
one neuron might add all of the inputs
and output 1 if the total is over 50, or 0 if it is not.
Another neuron might
assign weights to different input
signals, multiply each input signal by its weight, and add the results to give as its output. The outputs of these
neurons then pass as signals to the next layer. When the signals reach the final layer and its own
output is generated, the process is complete. The final signal can be interpreted
Now we have a simple
ANN, inspired by a simplified model of the brain, which can respond to a specific input with a specific output. It
doesn't really know what it is doing,. For simple tasks, ANNs can work well
with just a dozen neurons in a single hidden
layer.
Adding more neurons and layers allow ANNs to tackle more complex
problems. Deep learning refers to the
use of big ANNs, featuring at least two hidden layers, each containing many
neurons.
These layers allow the
ANN to develop more abstract conceptualisations of problems by splitting them
into smaller sub-problems, and to deliver more nuanced responses. It has been
suggested that three hidden layers are
enough to solve any kind of problem although, in practice, many ANNs include
millions of neurons organised in dozens of hidden layers.
By way of comparison,
human brains contain ~100 billion
neurons, cockroach brains ~1 million and snail brains ~10 thousand.
So if the 'deep' part
of deep learning is about the complexity of the ANN, what about the 'learning' part? Once the correct structure of the ANN
is in place, it needs to be trained. While in theory this can be done by hand, it would require a human
expert to painstakingly adjust neurons to reflect their own expertise of how to play a good
game.
Instead, a ML algorithm is applied to automate the process. The training process can be an
intensive and complicated process, and often never really ends, as constant
updates respond to new data availability and changes in the problem faced. Once a well-trained ANN is in place, it can be
applied to new data very quickly and efficiently.
Neural networks are
sometimes described in terms of their depth, including how many layers they
have between input and output, or the model's so-called hidden layers. This is
why the term neural network is used almost synonymously with deep learning.
They can also be described by the number of hidden nodes the model has or in
terms of how many inputs and outputs each node has. Variations on the classic
neural network design allow various forms of forward and backward propagation
of information among tiers.
Deep learning allows
the network to extract different features until it can recognize what it is
looking for. A neural network is a biologically-inspired programming paradigm
which enables a computer to learn from observational data.
Deep learning, uses
advanced computing power and special types of neural networks and applies them
to large amounts of data to learn, understand, and identify complicated
patterns. Automatic language translation and medical diagnoses are examples of
deep learning.
Even a computer
scientist would find it extremely hard to simply look inside a given deep
neural network and understand how that specific deep neural network actually
works.
Any given deep neural
network’s reasoning ability is pretty much embedded in the actual behavior of
hundreds and thousands of various simulated neurons.
These simulated neurons
are neatly arranged into, sometimes, dozens and sometimes hundreds of layers
which are intricately interconnected. AI has some secrets
that even its creators find hard to understand. This cannot continue if AI is
to become a part of our everyday life.
Each of the present the
simulated neurons, for example, in the very first layer receive a given input.
This input can be
anything. For example, one input
could be the intensity of a specific pixel in a specific image. After the simulated
neuron has received its input, it then has to perform a calculation.
Only after performing
that calculation can the simulated neurons output the new signal. After that, the deep
neural network system feeds the outputs from the first layer to the simulated
neurons of the second layer.
It is really a very
complex web of simulated neuron layers. The process continues
from one layer to the next layer until there are no more layers left. And when there are no
more layers left, that is the time when the deep neural network actually
produces an overall output.
In addition to that,
there is also a process that computer scientists call backpropagation.
This process is able to
tweak the calculations of various individual neurons. And it does that in a
way which enables the entire deep neural network to learn how to produce the
desired output.
Since the deep neural
network has many layers of simulated neurons, they enable the network to
recognize different things/objects at different levels of abstraction.
To take an example, if
a given deep neural network system is designed to recognize cats then the lower
layers of the deep neural network would recognize simpler features or things
about cats such as color and outlines.
Then, the higher layers
present in the deep neural network would recognize the more complex features. These would include stuff
like eyes and/or fur. After that, the topmost
simulated neurons-containing layer would identify all of that information as a
cat.
It should not be hard
to understand that computer scientists and engineers can take this approach and
apply it to a lot of other inputs (roughly speaking).
As mentioned before,
these are the same inputs that actually lead the given machine to learn and
teach itself. Using such techniques
machines can learn--
All the sounds which
make up the present words in a given speech
The words and the
letters which create various sentences in a given text.
The movements required
of a steering-wheel for proper driving.
Computer scientists and
researchers have used various but ingenious strategies to try and capture
information which they can use to explain in a lot more detail the things that
are happening in all such deep neural network systems.
Back in the year 2015,
researchers working at the search engine giant Google, modified one of their
image recognition computer algorithm which was based on deep learning.
When they did, the
algorithm changed the way it worked. So instead of the deep
learning algorithm trying to spot objects in various photos, the image
recognition algorithm would modify and/or generate them.
Researchers working at
Google effectively ran the image recognition deep learning algorithm in
reverse. This enabled them to
discover all the features that the program used in order to recognize, for
example, a building or a bird.
Researchers called the project
which enabled to produce the new images as Deep Dream.
The resulting photos
from the modified algorithm showed alien-like but grotesque animals that
emerged from plants and clouds. The modified images
also showed hallucinatory pagodas which bloomed across mountain ranges and
forests.
In other words, the
resulting photos/images provided good evidence that various deep learning
techniques were not, in fact, entirely inscrutable. The modified images
also revealed that that computer algorithms had little trouble in homing in on
various familiar visual features such as a given bird’s feathers and/or beaks.
However, those same
modified images also hinted at other important things. Other important things
such as how different human perception is from deep learning in the sense that
a deep learning algorithm could actually make something out of a given artifact
which humans would know to simply ignore.
Researchers working at
Google also noted that when their image recognition deep learning algorithm generated
photos of a given dumbbell, the algorithm also took the opportunity to generate
a human arm that held the generated dumbbell.
In other words, the
machine automatically came to the conclusion that a human arm was actually part
of the whole dumbbell thing. Computer scientists
have made much more progress by using ideas which were actually borrowed from
cognitive science as well as neuroscience.
Generative adversarial
networks (GANs) are deep neural net architectures comprised of two neural nets,
pitting one against the other (“adversarial”)
Generative Adversarial
Networks, or GANs for short, are an approach to generative modeling using deep
learning methods, such as convolutional neural networks.
GANs are a clever way
of training a generative model by framing the problem as a supervised learning
problem with two sub-models: the generator model that we train to generate new
examples, and the discriminator model that tries to classify examples as either
real (from the domain) or fake (generated).
The two models are trained together
in a zero-sum game, adversarial, until the discriminator model is fooled about
half the time, meaning the generator model is generating plausible examples.
The Generator is a
neural network trained on a distribution of data (where a distribution of data
might be images of dogs, cats, wolves) and extracts features from that
distribution of data (mixed with some level of randomness) to try to fool the
Discriminator into thinking the fake image is a real image.
That is, the
generator tries to create a fake cat image based upon the modeling (feature
extraction) of real cats to fool the Discriminator into thinking that the fake
cat image is a real cat image. Probably
sounds better when someone else explains it…
The Discriminator is a
convolutional neural network that is trained to predict whether input images
are real or fake. The discriminator outputs “1” if it thinks the image is real,
and “0” if it thinks the image is fake.
The discriminator tries to find a “decision
boundary” or clusters that separate images into the appropriate classifications
(dogs versus cats versus wolves). Then, to classify a new animal as either a
dog or a cat or a wolf, it checks on which side of the decision boundary it
falls and makes its prediction accordingly.
Made up of two
competing models which run in competition with one another, GANs are able to
capture and copy variations within a dataset.. The generator learns to
generate fake signals or data that can eventually fool the discriminator. The
input to the generator is noise, and the output is a synthesized signal
GANs can learn to mimic
any data distribution (diseased plants, cancer, skin lesions, tumors, broken
bones, mechanic parts abrasions) to create real-world like experiences. GANs
can create something totally new yet life-like based upon adding randomness to
a deep understanding of existing data distributions.
GANs’ potential is
huge, because they can learn to mimic any distribution of data. That is, GANs
can be taught to create worlds eerily similar to our own in any domain: images,
music, speech, prose. They are robot artists in a sense, and their output is
impressive – poignant even.
To understand GANs, you
should know how generative algorithms work, and for that, contrasting them with
discriminative algorithms is instructive. Discriminative algorithms try to
classify input data; that is, given the features of an instance of data, they
predict a label or category to which that data belongs.
One neural network,
called the generator, generates new data instances, while the other, the
discriminator, evaluates them for authenticity; i.e. the discriminator decides
whether each instance of data that it reviews belongs to the actual training
dataset or not.
Two neural networks
contest with each other in a game (in the sense of game theory, often but not
always in the form of a zero-sum game). Given a training set, this technique
learns to generate new data with the same statistics as the training set. For
example, a GAN trained on photographs can generate new photographs that look at
least superficially authentic to human observers, having many realistic
characteristics.
Generative Adversarial
Network (GAN) is a generic framework used to fool machine learning algorithms.
The generator and a discriminator tries to
outsmart the other. A generator modifies malicious version of the input it was
originally given and sends it to be classified by the IDS and the
discriminator. The goal of the generator is to fool an IDS, and the goal of the
discriminator is to mimic the IDS on classifying inputs (correct or wrong) and
provide feedback to the generator.
The game ends when the IDS and discriminator
cannot accurately classify the input created by the generator.
While GANs appear to be
very useful in creating new real-like content for the gaming, entertainment and
advertising industries, the bigger win might be in the ability to create
adaptive content that can accelerate training and education. Adaptive content
is individualized content that can drive personalized interactions based upon
an individual’s capabilities. experience, mood, and immediate goals.
In the area of
education, GANs could create dynamic adaptive content that could be
personalized for each individual student based upon their experience, areas of
expertise, and current areas of struggle.
The GAN-based educational system could dynamically create new content
that could help the student in areas in which they are struggling and could
push students into new areas more quickly if they are grasping concepts faster.
GANs might play an even bigger role in the retraining of our workforces
(especially when coupled with immersive technologies like Virtual Reality and
Augmented Reality) as technology advances the nature of work. Learning will no longer be dependent upon the
speed of the content and the expertise of the instructor!
And in the world of
healthcare, the ability to dynamically create new experiences in training and
re-certifying health care providers could ensure that all doctors are able to
leverage the last learnings and developments and not be dependent upon having
to attend the right conferences (snore) or churn through a stack of research
studies to find learnings that are relevant for their current level of
expertise.
GANs could also provide a
solution to the HIPAA privacy concerns by creating life-like data sets of
severe medical situations without actually having to disclose any particular
individual’s private data.
And in the area of
warfare, well, all sorts of bad things can be conceived with adaptive content
that can confuse enemies (and sway political elections).
.
The combination of deep
reinforcement learning (to create AI-based assistants) and GANs (to create
dynamic, adaptive content) could greatly accelerate the adoption and institutionalization
of new technologies and accelerate business and society benefits in the
process.
Deep Reinforcement Learning
could create AI-based assistants (agents) that aid teachers, physicians,
technicians, mechanics, engineers, etc. while GANs could create new adaptive
learning environments so that those assistants/agents are constantly learning –
learning while the human in the process is doing more human-related activities
like engaging with students, patients and customers.
The generator is trying
to generate images and fool and confuse the Discriminator into believing that
they are Real , and the discriminator tries distinguish the Real images from
the Fake ones generated by the Generator.
A Discriminative model models
the decision boundary between the classes. A Generative Model explicitly
models the actual distribution of each class.
In the end, the
generator network is outputting images that are indistinguishable from real
images for the discriminator.
Using Generative Models
on Neural Nets actually requires smaller number of parameters than the amount
of data usually a Neural Net requires to be trained on hence the model does a
good job at efficiently generating data.
Usually it is said that
if we want to train a Neural Network Model on small data sets with smaller
number of parameters we should use unsupervised pre training i.e train the
Neural Net in a unsupervised manner.
Otherwise generally a Neural Network model
requires a lot of data to be trained effectively and learn properly from the
data in order to avoid overfitting on small datsets.
C++ is preferred for
its speed and memory management, while Java's platform independency makes it an
opportune option for cross-platform development. Python, on the other hand, is
more like a human language with high readability, less complex syntax, and an
active community support
C++ as of today in its
efficiency, speed, and memory make it widely popular among coders. Java is
platform independent. It continues to add considerable value to the world of
software development. Python requires less typing, provides new libraries, fast
prototyping, and several other new features.
C++ Vs Java:
TOPIC
|
C++
|
Java
|
Memory Management
|
Use of pointers, structures, union
|
No use of pointers. Supports
references, thread and interfaces.
|
Libraries
|
Comparatively available with low
level functionalities
|
Wide range of classes for various
high level services
|
Multiple Inheritance
|
Provide both single and multiple
inheritance
|
Multiple inheritance is partially
done through interfaces
|
Operator Overloading
|
Supports operator overloading
|
It doesn’t support this feature
|
Documentation comment
|
C++ doesn’t support documentation
comment.
|
It supports documentation comment
(/**.. */) for source code
|
Program Handling
|
Functions and variables can reside
outside classes.
|
Functions and variables reside
only in classes,packages are used.
|
Portability
|
Platform dependent, must be
recompiled for different platform
|
Platform independent, byte code
generated works on every OS.
|
Thread Support
|
No built-in support for threads,
depends on libraries.
|
It has built-in thread support.
|
Python Vs Java:
Components can be developed in Java
and combined to form applications in Python. Let’s see some of the differences
in these two popular languages:
TOPIC
|
Java
|
Python
|
Compilation process
|
Java is a compiled programming
language
|
Python is an interpreted
programming language
|
Code Length
|
Longer lines of code as compared
to python.
|
3-5 times shorter than equivalent
Java programs.
|
Syntax Complexity
|
Define particular block by curly
braces,end statements by ;
|
No need of semi colons and curly
braces ,uses indentation
|
Ease of typing
|
Strongly typed, need to define the
exact datatype of variables
|
Dynamic, no need to define the
exact datatype of variables.
|
Speed of execution
|
Java is much faster than python in
terms of speed.
|
Expected to run slower than Java
programs
|
Multiple Inheritance
|
Multiple inheritance is partially
done through interfaces
|
Provide both single and multiple
inheritance
|
You can choose any language you want
i.e. the one you are comfortable to work with. Technically it depends upon the
job you want to accomplish. These 3 languages form the set of most popular
languages among the college graduates’ coders and developers. Stick with one language and achieve
perfection in that.
The Cloud AutoML API is
a suite of machine learning products that enables developers with limited
machine learning expertise to train high-quality models specific to their
business needs, by leveraging Google's state-of-the-art transfer learning, and
Neural Architecture Search technology.
AutoML also aims to
make the technology available to everybody rather than a select few
AutoML Vision's machine
learning code allows virtually anyone to provide the tagged images required to
train a system that is learning computer vision, enabling it to perform
categorization and other image recognition tasks. ... Vision is the first of a
number of planned Cloud AutoML offerings
Automated machine
learning, or AutoML, aims to reduce or eliminate the need for skilled data
scientists to build machine learning and deep learning models. Instead, an
AutoML system allows you to provide the labeled training data as input and
receive an optimized model as output.
Cloud AutoML, is a
point-and-click method for generating machine learning models without any
coding background.
The essence of Cloud
AutoML is that almost anyone can bring a catalogue of images, import tags for
the images, and create an operative machine learning model based on that.
Google does all the complicated operations behind the scenes, so the client
doesn’t require to comprehend anything about the complexities of neural network
design.
AutoML uses a simple graphical interface, enabling the user to drag in
a collection of images. Then, the platform needs to know how to represent those
images. Google does its work and users
end up with a model running in the cloud that can recognise the specified
courses in photos.
All of the big three
cloud services have some kind of AutoML. Amazon SageMaker does hyperparameter
tuning but doesn’t automatically try multiple models or perform feature
engineering.
Azure Machine Learning has both AutoML, which sweeps through
features and algorithms, and hyperparameter tuning, which you typically run on
the best algorithm chosen by AutoML.
Google Cloud AutoML, is deep transfer learning for language pair
translation, natural language classification, and image classification.
In deep learning,
transfer learning is a technique whereby a neural network model is first
trained on a problem similar to the problem that is being solved. One or more
layers from the trained model are then used in a new model trained on the
problem of interest.
BELOW: I WILL GIVE YOU A CONDORs EYE-VIEW OF CLOUD SYSTEMS LATER.. THESE TWO VIDEOS ARE JUST CHAATENE KE VAASTE.
https://www.csoonline.com/article/2130877/the-biggest-data-breaches-of-the-21st-century.html
CAPT AJIT VADAKAYIL
INTRODUCED THE CONCEPT OF VIRTUAL TEAMING AT SEA.
PEOPLE NEVER UNDERSTOOD
IT—
ALL THEY COULD SAY WAS “ AJIT, YOU ARE WAY AHEAD OF TIME”
When you give employees
the freedom to work remotely, not only will you increase employee satisfaction
and productivity, but you’ll also be able to grow your business with greater ease.
Moving your business to the Cloud makes it so that you’re able to securely
access critical data and applications from various devices – anytime, anywhere.
With a budding mobile workforce at-the-ready, your business will be
unstoppable.
Whereas in the past,
employees would access software that had been downloaded on their physical
computers, Cloud computing allows you to those same programs over the Internet.
The benefits include:--
Work remotely –
on-the-go or in the comfort of your home – and make the world your office
Enhance collaboration
among team members in real time
Easily scale your Cloud
capacity to match your current and ongoing needs
.
Cloud computing has
changed not only the way we collaborate, but where, when, and how we’re able to
work. The ability to clock in from any corner of the world in different time
zones , has given rise to a new professional culture that’s less about where
you are, and more about what you can offer.
The ascent of remote
working is fostering a more open, and more competitive, market; as candidates
are no longer so bound to their geographical location, there’s potential to
attract the best talent, no matter where they’re based.
https://www.news18.com/news/india/after-kudankulam-power-plant-isro-too-was-alerted-of-cyber-security-breach-by-dtrack-2375311.html
https://www.news18.com/news/india/after-kudankulam-power-plant-isro-too-was-alerted-of-cyber-security-breach-by-dtrack-2375311.html
IN MY ELDER SOs
WORKPLACE,YOU CAN WORK FROM HOME, WITHOUT PERMISSION.. YOU
ARE NOT REQUIRED IN OFFICE UNLESS THERE IS A CRITICAL TEAM MEETING..
WORK HOURS AT HOME ARE ABSOLUTELY FLEXIBLE.. MEMBERS OF THE OFFICE TEAM WILL BE "ON CALL" , IN WHICH CASE A CUSTOMER ( IN ANOTHER TIME ZONE ) WILL NEED YOUR ATTENTION ANY TIME AT NIGHT..
I WILL ADDRESS "CLOUD COMPUTING" IN GREAT DETAIL LATER...
WORK HOURS AT HOME ARE ABSOLUTELY FLEXIBLE.. MEMBERS OF THE OFFICE TEAM WILL BE "ON CALL" , IN WHICH CASE A CUSTOMER ( IN ANOTHER TIME ZONE ) WILL NEED YOUR ATTENTION ANY TIME AT NIGHT..
Cloud technology is
what enables employees to work from any location, giving them access via a
virtual environment to the same information that they’d have access to from the
office.
Collaboration
and communication tools ensure that your customers’ employees can use the
latest techniques and complete work at the same level as they would be able to
from their office desktop.
Employees can access
documents and data that they need by using specific credentials to maintain
security at the same time as giving them flexibility to work from home or any
other location.
This can be useful when your customers have multiple office
locations, such as if they have a primary central office and smaller remote
offices from additional locations.
Cloud technology provides a greater level of
flexibility and shared access to information centrally accessible via a single
server.
I WILL ADDRESS "CLOUD COMPUTING" IN GREAT DETAIL LATER...
ALL CLOUD DATA STORAGE
BIG PLAYERS ARE OWNED BY THE JEWISH DEEP STATE.. KOSHER BIG BROTHER KNOW EVERYTHING..
BIG-DATA MONOPOLISTS
ALREADY HOOVER UP BEHAVIORAL MARKERS AND CUES ON A SCALE AND WITH A FREQUENCY
THAT FEW OF US UNDERSTAND. THEY THEN ANALYZE, PACKAGE, AND SELL THAT DATA TO
THEIR PARTNERS.
BEING WIRED TOGETHER
WITH BILLIONS OF OTHER HUMANS IN VAST NETWORKS MEDIATED BY THINKING MACHINES IS
NOT AN EXPERIENCE THAT HUMANS HAVE ENJOYED BEFORE
BIG BROTHER KNOWS YUR
SEXUAL PREFERENCE BASED ON YOUR INTERNET SURFING AND DIGITAL PAYMENTS
NSA CAN DIP INTO CLOUD
DATA AT WILL.
WHILE GOOGLE HAS STATED
THAT IT WILL NOT PROVIDE PRIVATE DATA TO GOVERNMENT AGENCIES, THAT POLICY DOES
NOT EXTEND BEYOND AMERICA’S BORDERS.
GOOGLE
IS ALSO ACTIVELY WORKING WITH THE US INTELLIGENCE AND DEFENSE COMPLEX TO
INTEGRATE ITS AI CAPACITIES INTO WEAPONS PROGRAMS.
IT DOESN’T TAKE GREAT
MENTAL ACUITY TO IMAGINE WHAT FUTURE
BIG-TICKET COLLABORATIONS BETWEEN BIG-DATA COMPANIES AND GOVERNMENT
SURVEILLANCE AGENCIES MIGHT LOOK LIKE, OR TO BE FRIGHTENED OF WHERE THEY MIGHT
LEAD.
WITH COMPANIES LIKE OLA
/ UBER AND PIZZA DELIVERY DRONE
COMPANIES, HAVING DATA ON REAL TERRAIN, BIG
BROTHER DOES NOT NEED A GPS CONTROLLED MISSILE TO TAKE OUT A PRESIDENTS
BEDROOM.
THIS BLOGSITE HAS GIVEN
UP TELLING MODI THAT WE MUST HAVE OUR OWN CHIP FABLAB.. SECURITY CAMS IN ISRO/ NUCLEAR POWERPLANTS
ARE VULNERABLE.
IF YOU WEAR A
COMPROMISED FITBIT WATCH OR HIGH END MOBILE PHONE WHILE WALKING BIG BROTHER KNOWS WHERE YOU ARE..
WITH VPS TECHNOLOGY
KOSHER BIG BROTHER WILL KNOW WHERE YOU ARE HIDING AMONG SKYSCRAERS IN A CITY
LIKE NEW YORK
VPS IS LIKE A VISUAL
GPS, BUT RATHER THAN A CHIP THAT RECEIVES SATELLITE DATA, IT'S AN AI CLOUD SERVICE
THAT USES VISUAL DATA INPUTS TO LET CAMERAS PROVIDE ACCURATE POSITIONING AND
CONTEXTUAL INFORMATION.
GPS HAS A TECHNICAL
SHORTCOMING: IT CAN ONLY DETERMINE THE LOCATION OF THE DEVICE, NOT THE
ORIENTATION.
VPS DETERMINES THE
LOCATION OF A DEVICE BASED ON IMAGERY RATHER THAN GPS SIGNALS.
VPS FIRST CREATES A MAP BY TAKING A SERIES OF
IMAGES WHICH HAVE A KNOWN LOCATION AND ANALYZING THEM FOR KEY VISUAL FEATURES,
SUCH AS THE OUTLINE OF BUILDINGS OR BRIDGES, TO CREATE A LARGE SCALE AND FAST
SEARCHABLE INDEX OF THOSE VISUAL FEATURES.
TO LOCALIZE THE DEVICE,
VPS COMPARES THE FEATURES IN IMAGERY FROM THE PHONE TO THOSE IN THE VPS INDEX.
WITH THIS NEW
TECHNOLOGY, GOOGLE MAPS CAN MAKE USE OF THE USER’S PHONE CAMERA TO SPOT YOUR
SURROUNDINGS AND VISUALLY CONVERSE YOUR DIRECTION RIGHT IN FRONT OF YOUR EYES.
VPS CAN IN FACT PROVIDE
A CLOUD-BASED DEPOT OF ROBUST IMAGE DATA CAPTURED FROM PHYSICAL SURROUNDINGS.
BASICALLY, THE
TECHNOLOGY IS USEFUL IN URBAN AREAS WHERE GPS IS OFTEN BLOCKED BY SKYSCRAPERS.
- https://timesofindia.indiatimes.com/india/no-cabinet-meet-pm-uses-powers-to-revoke-article-356/articleshow/72204459.cms
https://indiankanoon.org/doc/8019/
TILL TODAY NOT ONE SINGLE COLLEGIUM JUDGE HAS BEEN ABLE TO UNDERSTAND THE INDIAN CONSTITUTION..
THE INDIAN CONSTITUTION IS A DIRECT LIFT OF THE BRITISH CONSTITUTION WRITTEN BY JEW ROTHSCHILD-- WITH SOME ADDITIONAL SOCIAL ENGINEERING CLAUSES WRITTEN BY BR AMBEDKAR ON DIRECTIONS OF COMMIE JEW JOHN DEWEY..
THE JUDICIARY HAS NOT UNDERSTOOD THAT THE INDIAN CONSTITUTION PROVIDES SUBJECTIVE VETO POWERS AND SUBJECTIVE DISCRETIONARY POWERS TO THE STATE GOVERNORS AND INDIAN PRESIDENT.. REST ARE ALL GIVEN ONLY "OBJECTIVE" POWERS ..
LIKE I SAID-- WHEN A SHIP IS VETTED BY AN OIL MAJOR , THERE WILL BE 999 OBJECTIVE QUESTIONS.. THE SHIP CAN PASS ALL THE 999 "OBJECTIVE" QUESTIONS..
THE FINAL "SUBJECTIVE" QUESTION TO THE VETTING INSPECTOR WILL BE " ARE YOU WILLING TO SAIL ON THIS SHIP WITHOUT RESERVATION FOR A VOYAGE NUDER THE PRESENT CAPTAIN AND CREW"..
IS THIS ANSWER I NEGATIVE , THE SHIP HAS FAILED..
THE JUDICIARY HAS NO POWERS TO CURB THE SUBJECTIVE POWERS VESTED IN THE PRESIDENT/ STATE GOVERNORS..
THE CAPTAIN OF A SHIP HAS "SUBJECTIVE" POWERS.. I MADE SURE THIS HAPPENED.. SUBCONSCIOUS BRAIN LOBE AND GUT FEELINGS ARE EVOKED HERE FOR A FINAL DECISION..
ARTIFICIAL INTELLIGENCE CAN NEVER INVOKE THE "SUBJECTIVE"..
THIS IS MY TIMELESS GIFT TO THE SEA.
https://ajitvadakayil.blogspot.com/2019/10/when-there-is-danger-of-his-ship.html
capt ajit vadakayil
..
THIS POST IS NOW CONTINUED TO PART 6 BELOW--
https://ajitvadakayil.blogspot.com/2019/11/what-artificial-intelligence-cannot-do_25.html
.
CAPT AJIT VADAKAYIL
..
.
.
ReplyDeletehttps://timesofindia.indiatimes.com/india/will-not-allow-nrc-in-bengal-there-will-be-no-division-on-the-basis-of-religion-mamata-banerjee/articleshow/72142017.cms
KICK OUT ALL ILLEGAL BANGLADESI MUSLIMS FROM BENGAL
Hi Captain,
DeleteI am from calcutta and can state that at least 30% of the population of West Bengal would be illegal Bangladeshi Muslims. The statistics for the other Indian cities such as Bangalore and New Delhi is the same. Once NRC is implemented and the illegal Bangladeshi Muslims are thrown out India will jump to a new level overnight.
Cities will become less crowded more open. Electricity costs and water costs will reduce. Less crime. On and on and on. I suspect there are up to 10 crore illegal bangladeshi muslims in India. They reproduce at an unbelievable rate in Bangladesh.
regards
SS
WE ASK MODI.. WHY HAVE YOU TAKEN JNU WOMAN NIRMALA SITARAMAN AS YOUR FINANCE MINISTER?
ReplyDeleteWILL SHE PAY OFF YOUR EXTRAVAGANT KOSHER PURCHASES ( LIKE RAFALE ) WITHOUT DEMUR? LIKE HOW ARUN JATLEY PAID OFF THE KOSHER BARAK MISSILE PURCHASES..
SENIOR CITIZENS ( NON-PENSION ) WHO HAVE WORKED THEIR ASSES OFF THEIR WHOLE LIVES ARE NOW IN SHIT STREET..
THEY HAVE PAID TAX-- DEDUCTED AT SOURCE BY THE COMPANY, AND THEN WHATEVER THEY SAVED FOR THEIR OLD AGE , THE INTEREST IS BEING TAXED AGAIN THIS TIME BY BANKS AT SOURCE..
WHY THIS DOUBLE TAXATION ?
IN KERALA OLD SENIOR CITIZENS HAVE TO PAY 900 RUPEES A DAY FOR UNSKILLED LABOUR --SAY TO CLEAN THE YARD..
CAPT AJIT VADAKAYIL HAS LOST HIS TRUST IN INDIAN BANKS AFTER THE PMC SCAM ..
I NOW PLAN TO SHIFT MY MONEY EARNED BY THE SWEAT OF MY BALLS AT SEA, INTO A FOREIGN BANK.. THIS IS WHAT YOU WANTED, RIGHT, NARENDRA DAMODARDAS MODI ? YOU HAVE GIVEN ROTHSCHILD A DRIVERS SEAT IN BANKING / INSURANCE ALL OVER AGAIN..
YOUR JEWISH MASTERS WILL SOON GIVE YOU A NOBEL PRIZE..AFTER ALL YOU WORE A SIKH TURBAN FOR ROTHSCHILD IN 1976..
capt ajit vadakayil
..
PUT ABOVE COMMENT IN WEBSITES OF--
PM MODI
PMO
AJIT DOVAL
RAW
NIA
ED
IB
CBI
AMIT SHAH
HOME MINISTRY
NEW CJI
GOGOI
ALL SUPREME COURT JUDGES
ATTORNEY GENERAL
ALL HIGH COURT CHIEF JUSTICES
CMs OF ALL INDIAN STATES
DGPs OF ALL STATES
GOVERNORS OF ALL STATES
PRESIDENT OF INDIA
VP OF INDIA
SPEAKER LOK SABHA
SPEAKER RAJYA SABHA
DEFENCE MINISTER - MINISTRY
ALL THREE ARMED FORCE CHIEFS.
RBI
RBI GOVERNOR
FINANCE MINISTER/ MINISTRY
RAJEEV CHANDRASHEKHAR
MOHANDAS PAI
NITI AYOG
AMITABH KANT
RAM MADHAV
RAJ THACKREY
UDDHAV THACKREY
VIVEK OBEROI
GAUTAM GAMBHIR
ASHOK PANDIT
ANUPAM KHER
KANGANA RANAUT
VIVEK AGNIHOTRI
KIRON KHER
MEENAKSHI LEKHI
SMRITI IRANI
PRASOON JOSHI
MADHUR BHANDARKAR
SWAPAN DASGUPTA
SONAL MANSINGH
MADHU KISHWAR
SUDHIR CHAUDHARY
GEN GD BAKSHI
SAMBIT PATRA
RSN SINGH
SWAMY
RAJIV MALHOTRA
THE QUINT
THE SCROLL
THE WIRE
THE PRINT
MK VENU
MADHU TREHAN
CLOSET COMMIE ARNAB GOSWMI
RAJDEEP SARDESAI
PAAGALIKA GHOSE
NAVIKA KUMAR
ANAND NARASIMHAN
SRINIVASAN JAIN
SONAL MEHROTRA KAPOOR
VIKRAM CHANDRA
NIDHI RAZDAN
FAYE DSOUZA
RAVISH KUMAR
PRANNOY JAMES ROY
AROON PURIE
VINEET JAIN
RAGHAV BAHL
SEEMA CHISTI
DILEEP PADGOANKAR
VIR SANGHVI
KARAN THAPAR
PRITISH NANDI
SHEKHAR GUPTA
SIDHARTH VARADARAJAN
ARUN SHOURIE
N RAM
NCW
REKHA SHARMA
SWATI MALLIWAL
CHETAN BHAGAT
I&B DEPT/ MINISTER
LAW MINISTER/ MINISTRY
WEBSITES OF DESH BHAKT LEADERS
SPREAD OF SOCIAL MEDIA
https://twitter.com/shree1082002/status/1197221761122623489
Deletesent and verified capt, grievance has been received by pmo
DeleteYour Registration Number is : PMOPG/E/2019/0668425
https://twitter.com/prashantjani777/status/1197540187724795905
DeleteYOU MUST WATCH THE NETFLIX SERIES EL CHAPO. IT IS SINISTER AND SLICK..
ReplyDeleteUNLIKE COLOMBIA , THE JEWISH OLIGARCHY DECIDES WHICH DRUG LORD WILL GET WHICH AREA.. THIS COMMUNICATION IS GIVEN IN A MEETINGS OF ALL DRUG LORDS ..
IT IS A MEXICAN GOVT ORDER , DO IT OR ELSE..
THE DEEP STATE SPONORS THE DRUGS FLOWING IN FROM MEXICO TO USA… THIS IS WHY ROTHSCHILDs MEDIA DOES NOT WANT A WALL.
https://en.wikipedia.org/wiki/Guadalajara_Cartel
EL CHAPO WAS A MERE DRIVER IN THE CARTEL OF JEW DRUG LORD MIGUEL ÁNGEL FÉLIX GALLARDO
HE WAS MADE THE BOSS OF BOSSES BY THE MEXICAN GOVT.
EL CHAPO WAS KING OF TUNNELS AT THE MEXICAN BORDER..
https://www.youtube.com/watch?v=m7AUT0TB2Wq
https://www.youtube.com/watch?v=_h1v7uEvFpE
https://en.wikipedia.org/wiki/Joaqu%C3%ADn_%22El_Chapo%22_Guzm%C3%A1n
EL CHAPO ESCAPED FROM JAIL BY A TUNNEL..
https://www.youtube.com/watch?v=ZLW11qfuY4I
GUADALAJARA AND MEXICO CITY IS INFESTED WITH JEWISH OLIGARCHS .. THEY WORK FOR THE DEEP STATE AND THEIR TENTACLES SPREAD EVERYWHERE CONTROLLING EVERYTHING IN MEXICO..
JEWS CONTROLLED THE BLACK SLAVE TRADE OF MEXICO.. THE JEWISH VAEZ ACEVEDO FAMILY WERE THE KINGPINS OF THE SLAVE TRADE..
.
http://ajitvadakayil.blogspot.com/2012/05/black-african-slave-trade-and-jews-capt.html
OVER THE APPROXIMATELY THREE HUNDRED YEARS IT LASTED, THE SLAVE TRADE BROUGHT ABOUT 320,000 AFRICANS TO THE COLONY.
MANY BLACKS WERE BORN IN MEXICO AND FOLLOWED THEIR PARENTS INTO SLAVERY. NOT UNTIL 1829 WAS THE INSTITUTION ABOLISHED BY THE LEADERS OF THE NEWLY INDEPENDENT NATION.
AFRICAN SLAVES LABORED IN THE SILVER MINES OF ZACATECAS, TAXCO, GUANAJUATO, AND PACHUCA IN THE NORTHERN AND CENTRAL REGIONS; ON THE SUGAR PLANTATIONS OF THE VALLE DE ORIZABA AND MORELOS IN THE SOUTH; IN THE TEXTILE FACTORIES ("OBRAJES") OF PUEBLA AND OAXACA ON THE WEST COAST AND IN MEXICO CITY; AND IN HOUSEHOLDS EVERYWHERE.
INDIANS REPLACED THESE BLACK SLAVES..
OUR NCERT SCHOOL HISTORY BOOKS MUST BE BURNT, AS IT GIVES ROTHSCHILD APPROVED HISTORY.. MODI AND HIS MINION KAYASTHA MINISTERS JAVEDEKAR/ PRASAD ENSURE WE ARE TAUGHT DEEP STATE SPONSORED FALSE HISTORY…
http://ajitvadakayil.blogspot.com/2010/04/indentured-coolie-slavery-reinvented.html
ROTHSCHILD SCREWED THE SPAINISH CATHOLIC CROWN..
http://ajitvadakayil.blogspot.com/2012/10/explosion-on-ss-maine-grooming-of.html
ALL MAJOR DRUG LORDS ( CAPOS ) IN MEXICO WERE JEWS …
THE BASIC STRUCTURE OF A DRUG CARTEL IS AS FOLLOWS:
FALCONS (SPANISH: HALCONES): CONSIDERED AS THE "EYES AND EARS" OF THE STREETS, THE "FALCONS" ARE THE LOWEST RANK IN ANY DRUG CARTEL. THEY ARE RESPONSIBLE FOR SUPERVISING AND REPORTING THE ACTIVITIES OF THE POLICE, THE MILITARY, AND RIVAL GROUPS.
HITMEN (SPANISH: SICARIOS): THE ARMED GROUP WITHIN THE DRUG CARTEL, RESPONSIBLE FOR CARRYING OUT ASSASSINATIONS, KIDNAPPINGS, THEFTS, AND EXTORTIONS, OPERATING PROTECTION RACKETS, AND DEFENDING THEIR PLAZA (TURF) FROM RIVAL GROUPS AND THE MILITARY.
LIEUTENANTS (SPANISH: TENIENTES): THE SECOND HIGHEST POSITION IN THE DRUG CARTEL ORGANIZATION, RESPONSIBLE FOR SUPERVISING THE HITMEN AND FALCONS WITHIN THEIR OWN TERRITORY. THEY ARE ALLOWED TO CARRY OUT LOW-PROFILE MURDERS WITHOUT PERMISSION FROM THEIR BOSSES.
DRUG LORDS (SPANISH: CAPOS): THE HIGHEST POSITION IN ANY DRUG CARTEL, RESPONSIBLE FOR SUPERVISING THE ENTIRE DRUG INDUSTRY, APPOINTING TERRITORIAL LEADERS, MAKING ALLIANCES, AND PLANNING HIGH-PROFILE MURDERS.
GUADALAJARA CARTEL WAS THE FIRST FULL-FLEDGED MEXICAN DRUG CARTEL, FROM WHICH MOST OF THE BIG CARTELS SPAWNED) .. SINALOA CARTEL SPAWNED FROM THE GUADALAJARA CARTEL
https://en.wikipedia.org/wiki/Sinaloa_Cartel
TIJUANA CARTEL SPAWNED FROM THE GUADALAJARA CARTEL
https://en.wikipedia.org/wiki/Tijuana_Cartel
CONTINUED TO 2--
CONTINUED FROM 1-
DeleteIN 2002 THE SINALOA CARTEL SAW THAT METH WAS THE NEXT BIG THING, AND STARTED TO BE ACTIVE IN CREATING THESE SUPER LABS AND MAKING THE METH IN MEXICO..
PRECURSOR EPHEDRINE FOR METH WENT FROM INDIA TO MEXICO IN HUGE QUANTITIES .. INDIAN POLITICIANS/ JUDGES/ POLICE/ WERE ALL INOLVED .
http://ajitvadakayil.blogspot.com/2017/02/breaking-bad-tv-serial-review-where.html
CHEMICALLY SPEAKING, METHYLAMINE IS JUST AMMONIA WITH ONE HYDROGEN ATOM SWAPPED OUT FOR A METHYL GROUP—A CARBON ATOM AND THREE HYDROGEN ATOMS.
JEW KING MAXIMILIAN I OF MEXICO ( YOUNGER BROTHER OF AUSTRIAN EMPEROR FRANZ JOSEPH I OF AUSTRIA ) IMPORTED RICH EUROPEAN JEWS TO MEXICO.
THE MEXICAN REVOLUTION ( 1910 ) DROVE AWAY THE BLOOD SUCKING JEWS .. BETWEEN 1918 AND 1920 THE JEWS SRATED COMING INTO MEXICO IN THOUSANDS FROM RUSSIA, POLAND, LITHUANIA, THE BALKANS AND THE MIDDLE EAST. TEN THOUSAND ARRIVED FROM EASTERN EUROPE TO THE PORT OF VERACRUZ AT THE INVITATION OF PRESIDENT JEW PLUTARCO ELÍAS CALLES.
THE MEXICAN INQUISITION SUCCEEDED IN ELIMINATING ALL VESTIGES OF OPEN JUDAISM IN MEXICO BUT THERE ARE AN ESTIMATED 24,000 MEXICANS WITH JEWISH ANCESTRY
THE OFFICIAL JEWISH POPULATION IN MEXICO WAS ESTIMATED AT 23,000 IN 1930..
THE CURRENT JEWISH POPULATION IS MORE THAN 65000, ABOUT 78% OF WHOM ARE IN MEXICO CITY , WHICH HAS MORE THAN 31 OPEN SYNAGOGUES. THERE ARE DOZENS OF HIDDEN SECRET ORTHODOX SYNAGOGUES ( MOSTLY HIDDEN INSIDE HOUSES ON AMSTERDAM AVENUE ) FOR THE CRYPTO JEWS .
.. THESE JEWS ARE ALL VERY RICH..
Capt ajit vadakayil
..
Large metros tend to have a solid jewish population. They control through these cities. They really are the devil's people. As they can give up their identity and assume the local identity even to the point of marrying locals.
DeleteCaptain sir..
ReplyDeleteI don't know if its time to expose this khalsa aid founder ravi Singh.. He has never given me a good vcibe and am a Sikh
Also he and another shady dude Peter virdee.. A rich dude and founder of London based B & S properties (proptery tycoon) have said to be donated thousands of crore for kartarpur corridor.
I don't know if that is for kartarpur or Pakistan.. Or they are naive..
How did this Peter virdee become so rich.. He has alot of links with Punjabi singers n actors.. And they visit him..
https://en.wikipedia.org/wiki/Ram%C3%B3n_Arellano_F%C3%A9lix
ReplyDeleteMAD JEW RAMON ARELLANO FELIX WAS THE MOST VIOLENT AND SADISTIC DRUG CARTEL LORD..
EL CHAPO KILLED HIM..
IT IS A LIE THAT A MEXICAN GOVT COP KILLED KIM ..
capt ajit vadakayil
..
SC in Ayodhya verdict gave the disputed area land to central govt. It also said the govt can give it back to Hindus if it likes.
ReplyDeleteSo, will Modi give this land to Hindus : NEVER!
He will excercise full control over this temple.
Also consider this - even if 1% Hindus donated Rs1000 for Ram Mandir , we have Rs 1000 crore.
So how much is govt going to spend on it?
Indeed Ayodhya too was grey judgement.
Modi babu will do Akshardam design for Ram Temple I guarantee u that!
DeleteWe need to look at Vishnu temples in far east for ideas. Temples from Cambodia / Vietnam can serve as reference design for this project.
DeleteI suspect they will deliver an amusement park or disneyland in the name of Ram Mandir.
Dear Ajit Sir,
ReplyDeleteEven before you could conclude by reaching part 10 of your AI series that Rajiv Malhotra has picked up from your blog & tweeted
See : https://twitter.com/RajivMessage/status/1197005391164366849?s=19
《Sorry, but pls dont mix up AI with artificial consciousness. One common diversion from the impending catastrophe is to over-abstract it such that you lose sight of the problem.》
He can't explain it properly at all.
Only Ajit Sir, you can explain it & kick the balls of this Malhotra.
Last time when I just simply put this comment that "You Rajiv Malhotra is plagiarizing from Captain Ajith Vadakayil Sir"
He didnot reply to me at all.
All he did is to block me.
That account is suspended.
This fellow Rajiv Malhotra's face is ridiculous to watch
A pathetic DP is used as Twitter scarecrow.
DONALD TRUMP LISTEN UP... AMD LISTEN GOOD...
ReplyDeleteUKRAINE IS USED BY DEEP STATE JEWS IN ISRAEL AS A SECOND HOMELAND DURING SUMMER SEASON..
AMBASSADOR TO EU JEW GORDON SONDLAND IS A DEEP STATE AGENT, JUST LIKE HILLARY CLINTON..
SONDLANDs PARENTS FAMILY IS AMONG THE NAZI GANG OF JEW HITLER WHO FLED GERMANY AFTER WORLD WAR.
http://ajitvadakayil.blogspot.com/2015/10/if-zionist-jews-created-isis-who.html
SONDLANDs FATHER SERVES IN ROTHSCHILDs FRENCH LEGION.. THIS THUG ARMY WAS USED TO ADVANCE THE INTERESTS OF JEWISH OLIGARCHS ALL OVER THE PLANET AND DO REGIME CHANGE IF THE NATIONs RULERS DO NOT ALLOW JEWS TO STEAL..
GORDON SONDLAND IS ONE OF THE DEEP STATE AGENTS WHO DOES NOT WANT THE MEXICAN WALL..
DONALD TRUMP--WATCH THE NETFLIX TV SERIAL " EL CHAPO", AND YOU WILL SEE THAT THE MEXICAN PRESIDENT AND THE DEA HEAD , ALONG WITH JEWISH OLIGARCHS DECIDE WHO WILL BE THE NEXT DRUG CARTEL BOSS OF BOSSES..
TRUMP- YOU MUST KNOW THAT MAXIMUM COCAINE IN ONE SINGLE BUILDING IS SNORTED IN THE WHITE HOUSE.
THE ALBANIAN JEWISH MAFIA CONTROLS THE COCAINE BUSINESS IN EU/ UK... COCAINE CAN BE TESTED IN THE SEWAGE DRAINS OF UK--SUCH IS THE HEAVY USE..
DEA DOES NOT PREVENT DRUGS FROM COMING TO USA—RATHER THEY CONTROL IT.. THREE US PREIDENTS REAGAN/ BUSH SENIOR / CLINTON ALLOWED COCAINE TO FLOOD INTO USA..
ALL DRUG CHANNEL TUNNELS ( MORE THAN 200 ) UNDER THE MEXICAN – US BORDER IS KNOWN TO DEA.
https://www.youtube.com/watch?v=kY9vd11smcQ
HILLARY CLINTON WON THE POPULAR VOTE IN THE LAST ELECTIONS ONLY BECAUSE OF MILLIONS OF SMURF ( MONEY LAUNDERING LESS THAN 10,000 USD LIMIT ) ILLEGAL MEXICAN IMMIGRANTS LET INTO USA BY CLINTON AND OBAMA.
UK IS THE DOGGING CAPITAL OF THE PLANET, WHERE SEX STARVED HOUSE WIVES OFFER FREE GANG BANG SEX ON THE STREETS TO PASSING DARK SKINNED TRUCK DRIVERS.. THEIR BODIES ARE SURCHARGED WITH COCAETHLENE ( COCAINE ALCOHOL MIX )..
COCAETHLENE DEATHS IN UK RE KEPT A SECRET AS COCAINE IS A STIMULANT AND ALCOHOL IS A DEPRESSANT..
EL CHAPOs SON WAS LET FREE AS A JOINT DECISION BY MEXICAN PRESIDENT/ JEWSIH OLIGARCHY AND DEA..
https://www.theguardian.com/world/2019/oct/26/mexico-27-drugs-cartel-suspects-released-a-week-after-el-chapos-son-freed-in-gun-battle
LAS VEGAS WAS CREATED TO LAUNDER DRUG MONEY..
DONALD THE TRUMP, IN MY 40 YEARS AT SEA, UKRAINE IS THE MOST CORRUPT NATION.. THANKS TO THE JEWISH OLIGRARCHS WHO CONTRL THIS SOULESS NATION.
Capt ajit vadakayil
..
PUT ABOVE COMMENT IN WEBSITES OF--
DeleteDONALD TRUMP
PUTIN
UK PM
AMBASSADOR TO FROM INDIA TO USA/ RUSSIA/ UK.
EXTERNAL AFFAIRS MINISTER/ MINISTRY
PMO
PM MODI
AJIT DOVAL
RAW
NIA
ED
IB
CBI
AMIT SHAH
HOME MINISTRY
NEW CJI
GOGOI
ALL SUPREME COURT JUDGES
ATTORNEY GENERAL
ALL HIGH COURT CHIEF JUSTICES
CMs OF ALL INDIAN STATES
DGPs OF ALL STATES
GOVERNORS OF ALL STATES
PRESIDENT OF INDIA
VP OF INDIA
SPEAKER LOK SABHA
SPEAKER RAJYA SABHA
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Sent direct messages to trump and putin from their official websites
DeleteSir, tweeted to -
Delete@IndianEmbassyUS @USAndIndia @POTUS @realDonaldTrump @BorisJohnson @HCI_London @UKinIndia @RuchiGhanashyam @harshvshringla @mfa_russia @IndianEmbassyUS @USAndIndia @POTUS @IndEmbMoscow @MEAIndia @DrSJaishankar @RusEmbIndia @IndianDiplomacy @indiandiplomats
& Central ministries and cabinet ministers,CMOs,DGPs,Armed forces,Law ministry,ED,NIA,Journalists,Commies,Spokespersons BJP/Congress,NCW,Shiva Sena,RSS,VHP,ABVP and many desh bhakt handls..
hillary did not get the popular vote ........the figures were manipulated by R in a last ditch attempt to get her elected .......in truth she got less than a third of what trump got [dems abandoned her big time].......by the way USA electorate is still under 100 million [even with so called illegals] is its real population is still under 200 million
DeleteCaptain,
ReplyDelete'No shame in showing tears, it's ok for men to cry: Sachin Tendulkar ..'
After his retirement ailaa made tea at home. now he is saying it's ok for men to cry.
next he will say....
what is he up to? he cannot be a commentator with his ailaaa voice. there is lot more time to become president of BCCI. what what what why why why
Read more at:
http://timesofindia.indiatimes.com/articleshow/72144812.cms?utm_source=contentofinterest&utm_medium=text&utm_campaign=cppst
THE HONGKONG PROTESTS ARE FUNDED AND CONTROLLED BY THE JEWISH OLIGARCHY..
ReplyDeleteEVEN CHINESE DO NOT KNOW THAT JEW MAO AND JEW MAURICE STRONG WERE DEEP STATE AGENTS...
JEWS WHO HAVE MONOPOLISED THE MAFIA AND CRIME IN HONGKONG DO NOT WANT TO BE EXTRADITED TO THE CHINESE MAINLAND..
MACAU GAMBLING IS FAR MORE THAN LAS VEGAS.. DRUG MONEY IS LAUNDERED HERE.
ROTHSCHILD CONTROLLED PORTUGAL LEGALIZED GAMBLING IN MACAU IN 1850..
ROTHSCHILD RULED INDIA..NOT THE BRITISH KING OR PARLIAMENT.. HE GREW OPIUM IN INDIA AND SOLD IT IN CHINA.. HIS DRUG MONEY WAS LAUNDERED IN HONGKONG HSBC BANK.
KATHIAWARI JEW GANDHI WAS ROTHSCHILDs AGENT WHEN IT CAME TO SUPPORTING OPIUM CULTIVATION IN INDIA..
http://ajitvadakayil.blogspot.com/2019/07/how-gandhi-converted-opium-to-indigo-in.html
INDIAN FARMERS WHO REFUSED TO CULTIVATE OPIUM WERE SHIPPED OFF ENMASSE AS SLAVES ABROAD WITH FAMILY..
http://ajitvadakayil.blogspot.com/2010/04/indentured-coolie-slavery-reinvented.html
INDIAN AND AMERICAN OLIGARCHY ( CRYPTO JEWS ) WERE ALL DRUG RUNNERS OF JEW ROTHSCHILD..
http://ajitvadakayil.blogspot.com/2010/11/drug-runners-of-india-capt-ajit.html
http://ajitvadakayil.blogspot.com/2010/12/dirty-secrets-of-boston-tea-party-capt.html
DRUG CARTELS OF COLOMBIA/ MEXICO USE HONGKONG TO LAUNDER THEIR DRUG MONEY..
GAMBLING TOURISM IS MACAU'S BIGGEST SOURCE OF REVENUE, MAKING UP MORE THAN 54% OF THE ECONOMY. VISITORS ARE MADE UP LARGELY OF CHINESE NATIONALS FROM MAINLAND CHINA AND HONG KONG.
HONGKONG IS NOW FLOODED WITH DRUGS .. DUE TO HIGH STRESS AT WORK, PEOPLE ARE ADDICTED .. HOUSE RENT IN HONGKONG IS VERY HIGH DUE TO THE JEWISH OLIGARCHS WHO CONTROL HONGKONG.
IN 2012, HSBC HOLDINGS PAID US$ 1.9 BILLION TO RESOLVE CLAIMS IT ALLOWED DRUG CARTELS IN MEXICO AND COLOMBIA TO LAUNDER PROCEEDS THROUGH ITS BANKS. HSBC WAS FOUNDED BY ROTHSCHILD.
CHINA'S EXCESSIVELY STRICT FOREIGN EXCHANGE CONTROLS ARE INDIRECTLY BREEDING MONEY LAUNDERING, PROVIDING A HUGE DEMAND FOR UNDERGROUND KOSHER MAFIA BANKS.
FACTORY MANUFACTURERS CONVERT HONG KONG DOLLARS AND RENMINBI WITH UNDERGROUND BANKS FOR CONVENIENCE WHILE CASINOS IN MACAU OFFER RECEIPTS TO GIVE LEGITIMACY TO SUSPECT CURRENCY FLOWS.
INDIA WAS NO 1 EXPORTED OF PRECURSOR CHEMICALS LIKE EPHEDRINE TO MEXICO FOR PRODUCING METH.. TODAY CHINA ( GUANGDONG ) HAS TAKEN OVER POLE POSITION..
http://ajitvadakayil.blogspot.com/2017/02/breaking-bad-tv-serial-review-where.html
EL CHAPO AND HIS DEPUTY IGNACIO "NACHO" CORONEL VILLARREAL USED HONG KONG TO LAUNDER BILLIONS OF DOLLARS..TO GET SOME IDEA WATCH NETFLIX SERIES “NARCOS MEXICO” AND “EL CHAPO”.
BALLS TO THE DECOY OF "FREEDOM " FOR HONGKONG CITIZENS.. IT IS ALL ABOUT FREEDOM FOR JEWISH MAFIA TO USE HONGKONG TO LAUNDER DRUG MONEY.
JEW ROTHSCHILD COULD SELL INDIAN OPIUM IN CHINA ONLY BECAUSE THE CHINESE MAFIA AND SEA PIRATES WAS CONTROLLED BY HIM AND JEW SASSOON.
COLOMBIAN/ MEXICAN DRUG CARTEL KINGS FEAR EXTRADITION TO USA.. SAME NOW WITH HONGKONG MONEY LAUNDERING MAFIA..
https://ajitvadakayil.blogspot.com/2019/11/paradox-redemption-victory-in-defeat.html
THE 2019 HONG KONG PROTESTS HAVE BEEN LARGELY DESCRIBED AS "LEADERLESS".. BALLS, IT IS 100% CONTROLLED BY JEWS
PROTESTERS COMMONLY USED LIHKG, ( LIKE REDDIT ) AN ONLINE FORUM, AN OPTIONALLY END-TO-END ENCRYPTED MESSAGING SERVICE, TO COMMUNICATE AND BRAINSTORM IDEAS FOR PROTESTS AND MAKE COLLECTIVE DECISIONS ..
THE KOSHER WEBSITE IS WELL-KNOWN FOR BEING THE ULTIMATE PLATFORM FOR DISCUSSING THE STRATEGIES FOR THE LEADERLESS ANTI-EXTRADITION BILL PROTESTS IN 2019..
CONTINUED TO 2-
CONTINUED FROM 1-
DeleteHONGKONG PROTESTERS USE LIHKG TO MICROMANAGE STRIKE STRATEGIES , CALL FOR BACKUP OR ARRANGE LOGISTICS SUPPLIES FOR THOSE ON THE FRONT LINES OF CLASHES WITH POLICE.
LIHKG CALLS ON RESIDENTS TO SKIP WORK AND CLASSES AND VANDALISE. HONGKONGERS STICK TO LIHKG AS POSTS ARE PREDOMINANTLY IN THEIR NATIVE TONGUE, CANTONESE.
LIHKG IS A SAFE HAVEN FOR THESE PROTESTING PEOPLE CONTROLLED BY JEWSIH OLIGARCHS.
AN ACCOUNT CAN ONLY BE CREATED WITH AN EMAIL ADDRESS PROVIDED BY AN INTERNET SERVICE PROVIDER OR HIGHER EDUCATION INSTITUTION, MEANING THE USER CANNOT HIDE THEIR IDENTITY FROM LIHKG.
THE JEWISH OLIGARCHS KNOW THEIR PRIVATE ARMY. THE FORUM DOES NOT REQUIRE USERS TO REVEAL ANY PERSONAL INFORMATION, INCLUDING THEIR NAMES, SO THEY CAN REMAIN ANONYMOUS.
LIHKG IS ALSO FERTILE GROUND FOR DOXXING PEOPLE NOT SUPPORTIVE OF THE MOVEMENT AGAINST THE EXTRADITION BILL. ONE POLICE OFFICER FOUND HIMSELF A TARGET OF PUBLIC MOCKERY WHEN HIS NAME AND PICTURE WERE LEAKED, ALONG WITH PRIVATE TINDER CONVERSATIONS REQUESTING SEXUAL FAVOURS IN A POLICE STATION.
THE PHRASE “BE WATER, MY FRIEND”, ORIGINALLY SAID BY MARTIAL ARTS LEGEND BRUCE LEE, HAS BECOME A MANTRA FOR PROTESTERS, WHO HAVE TAKEN A FLUID APPROACH TO THEIR RALLIES.
THE PHRASE HAS BEEN POPULARISED ON LIHKG AS A WAY TO PROVIDE ENCOURAGEMENT AND UNITE CITIZENS.
INDIAN JOURNALISTS ARE ALL STUPID POTHOLE EXPERTS, RIGHT ?
capt ajit vadakayil
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Sent to trump and putin...
DeleteLooks like Just Not Useful so called students are doing the same.
DeleteCapt, have sent your message to the Russian embassy in India via email to rusembindia@mid.ru
Deletehttps://ajitvadakayil.blogspot.com/2019/11/paradox-redemption-victory-in-defeat.html
ReplyDeleteSOMEBODY CALLED ME UP AND SAID
CAPTAIN, YOU ARE PARADOX REDEMPTOR..
CAN YOU GIVE A PARADOX WHICH WILL MAKE OUR COLLECTIVE BALLS AND TWATS GO TRRR PRRR BRRRR ?
WE UNDERSTAND THAT YOU PLAN TO DO IT ONLY AFTER YOUR REVELATIONS REACH 98%.. BUT ONE WEE PARADOX, CHAATNE KE VAASTE ? PLEAJJE !
WOKAY !
##############
THE WHITE INVADER RAPED OUR LOW CLASS BROWN INDIAN WOMEN AND PRODUCED A MULATTO ANGLO-INDIAN COMMUNITY ONE MILLION STRONG.
http://ajitvadakayil.blogspot.com/2014/02/devadasi-system-immoral-lie-of-temple.html
AT THE SAME TIME BROWN INDIAN SAILORS ( LASCARS ) WENT TO ENGLAND AND HAD CONSENSUAL SEX WITH WHITE WOMEN.. WHITE WOMEN LOVED SEX WITH INDIA SAILORS WHO WERE VIRILE , HAD LARGER CLEANER DONGS THAN THE SYPHILITIC WEE WHITE WILLIES , AND WHO DID NOT NEED ENDLESS MOUTH VACUUM..
http://ajitvadakayil.blogspot.com/2010/05/lascar-original-indian-merchant-navy.html
THE PARADOX IS ,
######### WHEN A INDIAN MAN SCREWED A WHITE WOMAN, THE CHILD HAD FERTILE BRAINS.
######## WHEN A WHITE AN SCREWED AN INDIAN WOMAN THE CHILD HAD SHIT FOR BRAINS..
EXAMPLE? AN ANGLO INDIAN MP OF TMC -- AKKAL KA DUSHMAN..
TEE HEEEEEE..
IT IS HIGH TIME WE ABOLISHED THE QUOTA FOR ANGLO-INDIANS IN THE INDIAN PARLIAMENT..
ANGLO-INDIANS ARE THE ONLY COMMUNITY THAT HAS ITS OWN REPRESENTATIVES NOMINATED TO THE LOK SABHA . THIS RIGHT WAS SECURED FROM JAWAHARLAL NEHRU BY FRANK ANTHONY, THE FIRST AND LONGTIME PRESIDENT OF THE ALL INDIA ANGLO-INDIAN ASSOCIATION.
THE COMMUNITY IS REPRESENTED BY TWO MEMBERS. FRANK ANTONY WAS THE CHAIRMAN OF THE ICSE COUNCIL WHICH MADE SURE WE TAUGHT ROTHSCHILD’S COOKED UP HISTORY IN OUR SCHOOLS.
http://ajitvadakayil.blogspot.com/2016/09/portuguese-inquisition-is-goa-by-jesuit.html
I WAS THE FIRST TO EXHUME THE GOAN INQUISITION BY JEW FRANCIS XAVIER.. TODAY WIKIPEDIA HAS A POST ON IT.
MY REVELATIONS NOW JUMP TO 60.10 %
capt ajit vadakayil
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Your Registration Number is : PMOPG/E/2019/0669490
DeleteSent emails..
Hello captain,
ReplyDeleteWe are giving free electricity to household and subsidised electricity to MSME even then the demand is falling.
How is it possible?
https://www.google.com/amp/s/indianexpress.com/article/business/133-units-switching-off-low-demand-behind-half-of-thermal-power-plants-shutting-down-6122166/lite/
Gratitude
Aditya
Dear Captain, what is your view on govt privatizing navratna companies like bpcl, scl, concor?
ReplyDeleteMaybe they might rool out different organizations
DeletePSU Banks are next after cleaning their mess with public money !!
DeleteDear Capt Ajit sir,
ReplyDeleteYou have opened up Pandora's box...similar tunnels would be existing in Punjab&Kashmir/Pakistan border for drug smuggling....if that's so, why it's not been curbed and the people booked so far ?
The man Dost Mohammad Khan of the Barakzai Dynasty who swallowed the Durrani Empire, was obviously a Jew. The Barakzai literally means son of Barak, the Israeli Jew King.
ReplyDeleteHow obvious could Rothschild go that retarded Historians could never gauge this? After handing over the empire to the British and Shah Shujah Durrani he was "exiled" to a comfortable land in India. And returned when Shujah Durrani was to be discarded. Rothschild got careless with his strategy which is now obvious everywhere.
DOST MOHAMMAD KHAN WAS A JEW AND AN AGENT OF JEW ROTHSCHILD.
DeleteDESCENDANTS OF PASHTUN JEWS AND PUNJABI CRYPTO JEWS ARE FIRST CLASS CITIZENS OF PAKISTAN.
MUHAJIRS ARE THE THIRD CLASS CITIZENS OF PAKISTAN, WHO ARE TREATED WORSE THAN SHIT. THEY DON’T ADMIT IT.. MY MUHAJIR PAKISTANI CHIEF OFFICER TOLD ME NAKED TRUTHS..
TWO DAYS AGO ALTAF HUSSAIN , ADMITTED IT TO ARNAB GOSWAMI..
AJIT DOVAL AND RAW HAS NEVER TRIED TO TELL NAKED TRUTHS TO INDIAN MUSLIMS.
RAW / CBI/ IB/ NIA HAS TO BE DISBANDED AND WE MUST BUILD UP A NEW FORCE WITH COMMITTED PATRIOTS..
THE NETFLIX TV SERIAL “ EL CHAPO “ MUST BE A PART OF THE TRAINING CURRICULUM.
THE “WAR ON DRUGS” DURING CLINTON/ OBAMA ERA WAS ALL ABOUT ELIMINATING ENEMIES OF THE CHOSEN DRUG CARTEL LORD ( BY CIA/ DEA/ MEXICAN PRESIDENT ).
EL CHAPOs 4TH WIFE EMMA BECAME BEAUTY QUEEN BECAUSE EL CHAPO BRIBED ALL JUDGES.
https://www.washingtonpost.com/nation/2019/11/19/reality-show-slammed-el-chapo-wife/
EL CHAPOs SON WAS RELEASED ON DIRECT ORDERS FROM THE MEXICAN PRESIDENT..
https://www.youtube.com/watch?v=TGjpZ4bGACw
I ASK MY READERS TO BINGE WATCH EL CHAPO NETFLIX SERIAL..
DURING EL CHAPOs TENURE AS DRUG EMPEROR , HE DUG HUNDREDS OF TUNNELS BETWEEN MEXICO AND USA.. THE DEA / CIA AND WHITE HOUSE TURNED A NELSONs EYE.
https://en.wikipedia.org/wiki/El_Chapo_(TV_series)
https://en.wikipedia.org/wiki/Joaqu%C3%ADn_%22El_Chapo%22_Guzm%C3%A1n
KHUSHWANT SINGHs FATHER SOBHA SINGH WAS A MASTER IN DIGGING TUNNELS FOR ROTHSCHILD UN THE DELHI LUTYENS AREA WERE WHITE RULERS WERE HOUSED..
WE ASK AJIT DOVAL… TRAIN JEW DARLING MODI IN WORLD INTRIGUE..
AJIT DOVAL,FIRST YOU YOURSELF HAVE TO TRAIN YOURSELF.. OUR SECURITY AGENCIES CANNOT BE STUPID IDIOTS..
IMPRISON RETIRED JUDGES/ JOURNALISTS WHO WERE AGENTS OF THE JEWISH DEEP STATE Y FORMING MILITARY COURTS ..
FOR MODIs JEWISH FRIENDS POWER/ MONEY IS GOD.. MODI THINKS THAT HIS JEWISH MASTERS LOVE HIM..
I WAS THE FIRST TO DECLARE THAT PRECURSOR EPHEDRINE FOR MEXICO METH DRUG CARTELS IS GOING FROM INDIA… RAW/ CBI/ NIA / ED DID NOT EVEN KNOW..
CHECK OUT COMMENT DATED JAN 6TH 2017 ABOUT BOLLYWOOD BIMBETTE MAMATA KULKARNI
http://ajitvadakayil.blogspot.com/2010/11/flying-coffin-mig-21-fishbeds-of-iaf.html
BOLLYWOOD IS FUNDED BY DRUG MONEY FROM PAKISTANI JEWS. TODAY MUMBAI POLICE AND ISI AGENT DRUG LORDS DANCE ON “UMANG DAY”..
https://www.youtube.com/watch?v=NS_MZDM1Jbs
capt ajit vadakayil
..
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Even in the current democratic & over-bureaucratic setup, it is possible for India to develop it's security-services especially intelligence-agencies.
DeleteTo do this, the Govt-of-India must establish a new service called "Indian-Security-Service" (ISS). This service must be an All-India-Service like the IAS and IPS. It must be created to enroll those people who want to work in security-agencies like CBI, IB, R&AW, NIA, Military-Intelligence, etc. It must be non-uniformed like IAS, wearing only normal-civilian-clothes.
The reason for doing so is because the IPS is a police-service which primarily focuses only on maintenance duties like law-&-order, detective-work, etc. which is not enough to develop an intelligence-agency level-official who needs to work on global-level competing with the likes of CIA/FBI, FSB/SVR/GRU, MI5/MI6, Mossad/Shinbet/Aman, etc.
Currently, they (IPS-personnel) can handle only things like protests, bandobast, curfews, arrests, riots, etc. They are not able to handle high-level conflicts such as global-politics, counter-intelligence, hegelian-dialects, global-intrigue, false-flags, etc.
The next-level skill to combat issues that India could face is severely-lacking. The IPS is not good enough to evolve to that level because consciously-&-subconsciously it is a policing-service, and even the bright-minds are forced to undergo the same training & service until they are senior-ranking (& unfortunately too old) enough to serve in the security-services like IB, CBI, R&AW, NIA, etc.
With the introduction of ISS, there is a direct-option for the bright-minds to be trained in global-intrigue, politics, intelligence, etc. while the IPS can continue to be the bastion of policing-duties like law-&-order and detective-work. To ensure that both-sides (ISS & IPS) understand each other's role-&-functioning, the fresh-graduates of ISS can be made to work for their first-year as probationary-officers in the police-service. But this would create issues because it would undo all the ISS training and also would again mentally-straightjacket their minds into police-level-thinking which was to be avoided by creating the ISS in the first-place. It is better if they start their careers with security-agencies like CBI which is a domestic-level-investigation-agency and then maybe get promoted/transferred to the more able intelligence-agencies like IB, R&AW, etc.
All the intelligence-agencies must preferably allow the inclusion of a few IPS-officers too in a form of promotion-system where the senior-IPS (or a worthy-IPS officer) is included as part of teams in the agencies. This will ensure that a different-POV is brought in by the IPS because otherwise the ISS would only dominate and they could miss-out certain things which an IPS-officer can realize due to his/her experience of policing.
The IPS itself must be reduced to only a police-service. The services like CRPF, CISF, BSF, ITBP, etc. must allow promotions from within the ranks to the top (as of 2019, Central-Govt is working on allowing promotion from within the ranks). This will empower-&-motivate them, while at the same time reducing the troubles faced by (& sometimes due to) IPS-officers. The IPS will then be relegated to a Central-Govt police-service since all other security-services are freed from it's domain. This is a positive development since it allows the IPS to focus on the main job of policing rather than try to be a "jack-of-all-trades-master-of-none" which it has been since it's inception.
Civil services including IPS are british designed structures to promote divide and rule. Every district of India has a SP of police who is non local and hails from a different state altogether. Immidiately before assuming office these SP s were nothing but book worms preparing for civil service.
DeleteEvery districts has locals as constables but not inspectors and above. If a constable wants to take a promotion as officer he has to take transfer to other district. Kerela has a odia DGP and odisha has a Bihari DGP. Though it has its positive aspects too , it leads to these officers being insensitive to local sentiments.
If loknath Behera was DGP of Odisha and a situation similar to sabraimala was happening in Jagannath puri ( allowing of non hindus in the temple eg ) he would have been hesitant to apply govt orders. He would have been afraid of direct public reaction against him in his own home town in Odisha.
He got away from common malayali peoples anger as no malayali would bother to come to Odisha in his home town to protest,as it is not practical.
In England every SP starts as a constable till he becomes SP on merit.
In ancient India SP level officers were called Dandnayak or Mahadandnayak. If he failed to capture a thief he had to compensate the victim from his own pocket. If he failed to catch a murderer he risked loosing his life to the king.
There is a lack of ownership in India starting from PM down to an ordinary clerck in the govt.
All top civil servants are migrants essentially. Only the Mps and Mlas are people with power who are not migrants, but thses people are usually the school backbenchers types and nothing awesome can be expected of them.
Sir, Sent emails ..
DeleteTweeted to central ministries,MEA, security agencies, DGPs, RSS and VHP ..
@Ramas All Bullshit narration
DeleteFirst of all Loknath Behera is a Converted Christian
Do your research properly
Next, you are citing Puri.
When the Mutts in Puri were demolished, there was no damn local protest.
Nothing
A bunch of Cowards stay in Odisha.
A damn bunch of bloody cowards.
The Odia politicians are puppet at the hands of Tamilian fellow & a king maker, Kartikeyan Pandian.
Pandian is real CM.
Naveen Patnaik is a naam ke vaste on the chair.
When the Odia people didn't object to Mutt demolition the way it actually is needed says it all that if Non-Hindus are allowed to enter Shri Jagannath Temple then Odia people will spread Red Carpet to welcome them.
Regarding the Sanctity of Shri Jagannatha Temple, it's the South Indians who joined hands to rescue the Temple's Mutt.
I am involved directly into all this information, getting in advance & alerting it.
Tell me? there is police already deployed in Shri Jagannatha to resume demolition of Mutts.
Why it is unable to resume the task of demolition?
Any idea?
Who is stopping it?
Sabarimala caught up National attention & the voice became huge enough but looking at people's participation, statistics clearly states that South India still outpace the number of people concerned compared to rest of India.
@IndicCollective based from Chennai helped slap legal case for demolition of Mutt.
Likewise it's the direct Tweet of Capt.Amarinder Singh who stopped the demolition of Mangu Mutt.
Only a handful of Odia people counted on finger tips were only concern regarding Mutt demolition.
Rest are Cowards.
Odia people of late became cowards.
Disgraceful.
Received below from home affairs regarding banning of palm oil imports from Malaysia and replace with Indonesia
ReplyDeleteNo.6/03/2016-EP(Agri-VI)
Department of Commerce
EP(Agri-VI)
Room No. 445-A, Udyog Bhawan
20th November, 2019
New Delhi, Dated
То,
Sh. Debdoot Sarkar,
Subject: Total ban of Paim Oil imports from Malaysia and
replace Imports from Malaysia to Indonesia - reg.
I am directed to refer to your grievance No.MINHA/E/2019/07876 dated 17th October, 2019 on the above
mentioned subject and to say that your suggestion has been noted and will be considered as appropriate time.
Thanking you,
निधि शर्मा/(Nidhi Sharma)
अनुभागअधिकारी/Section Officer
/Email: moc epagri3@nic.in
टेलीफैक्स/ Telefax: 23061621
Capt. Ajit Vadakayil January 15, 2017 at 1:14 AM
ReplyDeletehttps://www.youtube.com/watch?v=K78t2-W2jag
AND HERE YOU MAY FIND FROGS UP THE ASSHOLE
THESE FAKE GURUS DO NOT KNOW THAT LEVITATION IS NOT OF THE BODY ( CARCASS ) BUT OF THE SOUL
Capt ajit vadakayil
..
################
Capt. Ajit Vadakayil January 26, 2017 at 8:10 PM
https://www.youtube.com/watch?v=tfrQwmI2-VQ
CHECK 30.10 OF ABOVE VIDEO
THIS BLOGSITE WARNS NITYANANDA
GIVE AN EXPLANATION FOR THIS ROGUE WOMAN VOMITING OUR DIAMONDS AND PEARLS
IS THIS BULLSHIT VIDEO PUBLISHED BY YOUR ENEMIES ?
OR IT YOUR OWN ?
IS THIS WOMAN YOUR DISCIPLE ? HAVE YOU AUTHORISED HER ?
AND WHAT BULLSHIT ( LEVITATION ) IS THIS ?
ONLY 12 STRAND DNA MAHARISHIS ( NIL JUNK ) CAN DO LEVITATION.
NITYANANDA -- DONT YOU KNOW LEVITATION IS OF THE SOUL AND NOT THE CADAVER ?
https://www.youtube.com/watch?v=K78t2-W2jag
DO FORCE ME TO COME HAMMER AND TONGS AFTER YOU --I WILL NOT ALLOW YOU TO DESTROY THE TRUST HINDUS HAVE IN SANATANA DHARMA
YOU WILL HAVE NOWHERE TO HIDE NITYANANDA
WATCH THIS SPACE
PUT THIS COMMENT IN NITYANANDAs AND RAJIV MALHOTRAs WEBSITE
ASK FOR AN ACK
capt ajit vadakayil
..
Captain, White people are always present in his ashram. Hindus do not donate much to his ashram after his scandal in 2010. He should be thriving by foreigners donations. He is funded to make tamilnadu hindus ashamed of hinduism.
DeleteDear Capt Ajit sir,
DeleteI know that lady who vomits Diamonds/pearls...she's the daughter of Nithyananda's Dubai chapter who is a famous veterinary doctor minting money with vaccinations there....who was earlier core member of Sai Baba long back, then drifted to Nithyananda to make money with him using hedge funds/investments globally.
Even the parents of the 2 daughters missing are no means innocent, as they wilfully and knowingly abused their daughters to do non-sensical things in the ashram, when asked to return, they refused and said we will live as long as possible inside the dirty ashram life drudgery as destined.
Sent DM on facebook -
Deletehttps://www.facebook.com/ParamahamsaNithyananda/
on twitter -
https://twitter.com/SriNithyananda/status/1196964815257780224
@SriNithyananda
@RajivMessage
Nithyananda could continue inspite of being a fraud is bcoz it is all a battle of fraud of one camp to a fraud of another camp.
DeleteEverytime a Muslim or a Commie would ridicule Nithyananda, Vasudev, Triple Shri or anyother Godman , the very next moment another video of hilarious Muslim rituals or Church Congregation would moderate the criticism.
In the battle for तो क्या हो गया from Hindus vs तो क्या हो गया from Abrahmics;
Finally nothing used to happen & these Selfstyled Godmen kept enjoying the benefit.
Also there are political nexus as donation by idiot people involves so much of money out of which a part goes to politicians who remain tight lipped.
After I joined Twitter, I could only find frauds, opportunist, freeloaders & abusers on SM.
Pathetic
Till date people are tagging & putting Conman Gaurav Pradhan as their favourite to a question of whom people like on Twitter.
Again I have started seeking people's help to expose that fellow.
No idea if it will work.
Pradhan has been mass blocking.
Twitter is full of Frauds, almost 99.99% are hypocrites.
When Ajit Sir was on break it was an extremely painful time(I swear), bcoz involving with fraudsters over Twitter, the heart was searching for a clean space.
After blogging got resumed there is so much of relief.
I may not put many comments but atleast I can read the conversations here.
Captain, is it a good-idea for Govt to pay more wholesome salaries but remove perks like accommodation, pensions, etc. ?
ReplyDeleteBecause the perks like accommodation, canteen, etc. are a big bureaucratic mess with lot of corruption. And the pensions are a whole mess to itself with the Govt having so many services meaning a huge workforce has to be maintained to simply administer the pension-schemes. Also, the pension-schemes have their irregularities which gives opportunities for OROP like issues to be created by those who want to stir trouble.
Is it worthwhile to have a private-company attitude wherein a lump-some salary is paid with nothing else ? And if the Govt has to provide any perk shouldn't it be something like a Health-Insurance plan (by Govt-company like LIC, not private) so that retired-employees can benefit from not having tension due to fear of unexpected health-issue which can cause financial-ruin for them in old-age ?
http://ajitvadakayil.blogspot.com/2018/03/treason-from-within-biggest-cut-on.html
ReplyDeleteELECTORAL BONDS ARE USED TO LAUNDER DRUG MONEY..
ANONYMOUS FUNDING ( EVEN FROM KOSHER ARMS DEALERS ) IS BEING FUNNELED TO THE COFFERS OF POLITICAL PARTIES THROUGH BEARER INSTRUMENTS ISSUED BY THE STATE BANK OF INDIA.
SINCE THE INTRODUCTION OF THE SCHEME BY THEN FINANCE MINISTER ARUN JAITLEY, ELECTORAL BONDS WORTH RS 6,000 CRORE HAVE BEEN SOLD SO FAR. OF THE FIRST TRANCHE OF RS 222 CRORE, 95 PER CENT OF THE FUNDS HAD GONE TO THE RULING BJP
THE RESERVE BANK OF INDIA’S (RBI’S) REPEATED WARNINGS ON THE ELECTORAL BOND SCHEME IN BEARER FORM HAVING THE POTENTIAL TO INCREASE BLACK MONEY CIRCULATION, MONEY LAUNDERING, CROSS-BORDER COUNTERFEITING AND FORGERY WERE IGNORED..
WE ASK , WHY IS INDIA BUYING INFERIOR AND EXPENSIVE ARMS FROM JEWSIH COMPANIES ? KOHSER MANUFACTURERS OF INFERIOR AND EXPENSIVE ARMS , GIVE 35% KICKBACKS.. IF YOU BUY 10,000 CRORES OF ARMS , 3500 CRORES IS YOURS..
CHAKKAR KYA HAI?
PAKISTANI ISI IS DONATING MONEY TO INDIAN POLITICAL PARTIES SUPPORTING NAXALS AN ISLAMIC TERRORISTS..
LAVISH EXPENDITURE IN THE ELECTIONS ( FUNDED EVEN BY PAKISTAN AND CHINA ) IS THE MOST CRITICAL PROBLEMS IN INDIA’S ELECTORAL SYSTEM. CEC IS PRETENDING TO SLEEP AND EXPECTING "WE THE PEOPLE" TO WAKE HIM UP..
THE USAGE OF BLACK MONEY DURING ELECTIONS NOT ONLY ALLOWS FOR LAUNDERING, IT ALSO HAS SERIOUS ECONOMIC AND POLITICAL COSTS ATTACHED TO IT. IT LEADS TO A POLITICAL SUPPORT TO CORRUPTION AND CRONY CAPITALISM.
http://ajitvadakayil.blogspot.com/2012/11/kleptocracy-in-india-lobbying-pimping.html
HOW DID THIS FELLOW SURESH NANDA GET UBER RICH?
https://en.wikipedia.org/wiki/Suresh_Nanda
IT ALLOWS FOR CRIMINAL-POLITICAL NEXUS AS THE EXTORTIONISTS AND GOONS ARE ABLE TO BUY PARTY TICKETS AND THEREFORE, THE LAW-BREAKERS BECOME THE LAWMAKERS IN OUR COUNTRY.
WE ASK THE CEC AND EC TO WATCH THE NETFLIX SERIAL “EL CHAPO”..
https://ajitvadakayil.blogspot.com/2019/11/paradox-redemption-victory-in-defeat.html
ANONYMITY: NEITHER THE DONOR (WHO COULD BE AN INDIVIDUAL OR A CORPORATE) NOR THE POLITICAL PARTY IS OBLIGATED TO REVEAL WHOM THE DONATION COMES FROM. THIS UNDERCUTS A FUNDAMENTAL CONSTITUTIONAL PRINCIPLE: THE FREEDOM OF POLITICAL INFORMATION, WHICH IS AN INTEGRAL ELEMENT OF ARTICLE 19(1) (A) OF THE CONSTITUTION.
ASYMMETRICALLY OPAQUE: BECAUSE THE BONDS ARE PURCHASED THROUGH THE SBI, THE GOVERNMENT IS ALWAYS IN A POSITION TO KNOW WHO THE DONOR IS. THIS ASYMMETRY OF INFORMATION THREATENS TO COLOUR THE PROCESS IN FAVOUR OF WHICHEVER POLITICAL PARTY IS RULING AT THE TIME.
WE THE PEOPLE WATCH !
capt ajit vadakayil
..
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Sir, sent emails and DM on Nirmala Sitaraman's FB page
Deletehttps://www.facebook.com/nirmala.sitharaman/
Ajit Garu, Namaskaram
ReplyDeleteAfter your post on Kalaripayattu it has gone global. Many ppl r trying to learn now. Just by watching these videos one can get how deadly kalari was in its heydays when it is actually used in warfare. That is the reason u r not afraid to take on ppl in a fist fight. Kalaripayattu moulds the attitude itself.
https://www.youtube.com/watch?v=WEQ1Hpibekg
By watching Kungfu training with all the hoo-haa sounds seems trivial and comical infront of Kalari as per below video.
https://www.youtube.com/watch?v=fnypyWf3NzE
GOI should seriously think of atleast training children in the 1st part Maipayat with proper guru induction. It can lift the Kshatriya spirits of many ppl.
Regards,
Ravi
HINDU JULIUS CAESARs SON WITH KERALA THIYYA QUEEN CLEOPATRA WAS SENT TO CALICUT KING FOR HIS SAFETY ( FROM MURDERERS )..
Deletehttp://ajitvadakayil.blogspot.com/2019/08/secrets-of-roman-pantheon-inaugurated.html
HE BROUGHT FOR IDENTIFICATION A SWORD MADE OF WOOTZ STEEL MANUFACTURED IN CALICUT AND PRESENTED TO JULIUS CAESAR BY THE KING OF CALICUT WITH A ROYAL INSIGNIA...
THIS WAS A WAVY BLADE , WITH SHIVA LINGAM POWDER INFUSED INTO METALLURGY. IT WAS CALLED "SWORD OF KRISHNA". THIS THRUSTING SWORD COULD PENETRATE STEEL ARMOUR LIKE BUTTER.
THIS SWORD WAS ALWAYS HIDDEN BEHIND -- AT THE WARRIORs SPINE ..
THIS SWORD COULD BE POINTED TO A MARMA ON THE FOREHEAD , AND IT TAKES EFFECT EVEN WITHOUT REAL CONTACT.. ONLY A WARRIOR WITH HUGE AURA ( HIGH SOUL FREQUENCY ) CAN DO THIS..
http://ajitvadakayil.blogspot.com/2017/11/urumi-invincible-kalari-sword-wootz.html
TODAY CURVED BLADE SWORDS ARE CALLED "KRIS" / "KERIS".
https://www.youtube.com/watch?v=zy4Rdk_8vYI
capt ajit vadakayil
..
Salarjung Museum has an egyptian mummy in display, I was excited when I saw it as a kid. I wonder why the Nizam was interested in a mummy.
DeleteYour Registration Number is : PMOPG/E/2019/0669632
ReplyDeleteCaptain this book by Gary Webb called Dark Alliance is worth a read,have not yet finished it,it exposes the links between CIA and cocaine trade in various parts of South America and how cocaine trade helped fund rebel groups.
ReplyDeleteCIA IS DIVIDED INTO TWO PARTS
DeleteFIRST PART IS LAW ABIDING
SECOND PART IS ROGUE WHICH IS A TOOL OF THE JEWISH DEEP STATE
IN MEXICO THERE WAS A DRUG CARTEL WAR BETWEEN EL CHAPO ( SINALOA CARTEL ) AND AURELIO CANO FLORES ( GULF CARTEL ) ..
CIA / DEA / MEXICAN PRESIDENT SUPPORTED EL CHAPO..
https://en.wikipedia.org/wiki/Aurelio_Cano_Flores
https://en.wikipedia.org/wiki/Gulf_Cartel
https://en.wikipedia.org/wiki/2012_Nuevo_Laredo_massacres
SINALOA WAS THE BIGGEST SUPPLIER OF COCAINE/ METH / HEROIN TO THE US DURING EL CHAPO’S LONG REIGN AS LEADER
BOTH GULF AND SINALOA CARTEL USED UZI ISRAELI GUNS..
WHEN THE EL CHAPO’S SON WAS ARRESTED BY THE SECURITY FORCES IN OCTOBER 2019, SINALOA CARTEL GUNMEN WERE QUICK TO DEMONSTRATE THE GROUP'S SERIOUS MILITARY MIGHT.
THEY FOUGHT STREET BATTLES WITH THE ARMY IN BROAD DAYLIGHT, SET FIRE TO VEHICLES, AND EVEN STAGED A PRISON BREAK BEFORE THEIR LEADER WAS EVENTUALLY FREED ( ON MEXICAN PRESIDENTs ORDERS ) .
IT WAS A SIGN THE GROUP REMAINS AN IMMENSELY POWERFUL FORCE.
https://en.wikipedia.org/wiki/Los_Zetas
https://en.wikipedia.org/wiki/Vicente_Carrillo_Fuentes
https://en.wikipedia.org/wiki/Ju%C3%A1rez_Cartel
capt ajit vadakayil
..
Dear Capt Ajit sir,
ReplyDeleteNetanyahu is indicted for fraud, bribery, breach of trust...will fave trial...what will happen to his friend Modi...sangathvam matters.... will crush him too...what do you say ?
Huge GST fraud going on and Modi/FM are silent. 500 crore in Kolkata recently; all Marwaris and it is hushed up immediately - one day news coverage. The loot is on.
DeleteModi is doing a great job in preparing India for Rothschild's New World Order...he will be allowed to retire and live like royalty.
Deletehttps://timesofindia.indiatimes.com/city/delhi/regret-relief-as-145-us-deportees-land-at-delhis-igi-airport-with-hands-and-feet-tied/articleshow/72151975.cms
ReplyDeleteTHESE BASTARDS DESERVE TO BE HUMILIATED..
http://www.sarovic.com/jacob_rothschild_is_guilty.htm
ReplyDeletewarren buffet is a front man of jacob rothchild in usa?Rotchild supports charity by denouncing that warren buffet will donate 85 percent of his wealth after his death
Politician have already made this country slaves of foreign rules with no long term vision, they keep on inciting voters based on their tribe, caste and religion and supreme court also take part in it.
ReplyDeleteWhereas real work of Govt is to provide social security so that man and woman can make their Gnaati , Samaj or Tribe strong, so that they can fight from the outside forces which destroy our nation.
But here Govt and Supreme Court together is allowing American MNC to exploit Indian Citizen by making them feel that they lack hence they have to work in Contractual Agreement
What about HDI , compare it Bhutan.
You cannot inject love virus from #Bollywood movie and giving unwanted freedom to woman in the namw of #Feminism to destroy the customary law of each tribe, caste and Gnaati and then say country is progressing
#Tribalism and #Casteism both is good, but Govt servant who do not work for the #Beneficiary but work for #Politician or #CorruptBureacrats , they are bad as they are least afraid of getting removed from the job and thus treat each and every citizen as slave and do just #TimePass in confusing their bosses in the meeting with half knowledge
https://youtu.be/AiqEvYeoR2E
ReplyDeleteThese are our indian cows
SOMEBODY ASKED ME
ReplyDeleteIF BJP IS GETTING 95% DONATIONS BY ELECTORAL BONDS -- AND REST ALL POLITICAL PARTIES HAVE GOT ONLY 5% OF THE DONATION PIE .. DOES THIS MEAN OTHER PARTIES LIKE CONGRESS ARE STARVED OF ELECTION FUNDS ?
SORRY
ALL THIS MEANS IS OTHER PARTIES ARE GETTING CASH ILLEGALLY FROM PAKISTANI ISI, CHINESE , SAUDI JEWISH WAHABBIS ETC..
YESTERDAY YOU SHOULD HAVE SEEN HOW YOGENDRA YADAV WENT INTO SELF RIGHTEOUS MODE -- I WOULD NOT EVEN FART ON THIS CHOOTs FACE..
DAY NIGHT PINK BALL TEST CRICKET IS A STUPID IDEA
ReplyDeleteFATIGUE SETS IN DURING TEST CRICKET-- DEW IS DANGEROUS
PLAYERS MAY DIE
http://ajitvadakayil.blogspot.com/2013/04/swinging-cricket-ball-curving-soccer.html
yes Captain.
Deletethey should leave test cricket alone. t20 etc circuses are there for monkeying.
reminds me ailaa proposed an idea of 2 innings in a odi, 25 overs each :(
https://twitter.com/realDonaldTrump/status/1197573996960727041?ref_src=twsrc%5Egoogle%7Ctwcamp%5Eserp%7Ctwgr%5Etweet
ReplyDeleteGREAT IRANIAN PEOPLE ARE DONE WITH JEWS RULING THEIR NATION ...
http://ajitvadakayil.blogspot.com/2017/04/the-most-evil-journalist-capt-ajit.html
capt ajit vadakayil
..
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From/to Iran -
Deletehoc.tehran@mea.gov.in
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Iranemb.del@mfa.gov.ir
Iranemb.del@mfa.gov.ir
https://twitter.com/tuki00108/status/1197722600589283328
https://twitter.com/tuki00108/status/1197721057857159168
https://twitter.com/tuki00108/status/1197719958261649408
Dear Capt Ajit sir,
ReplyDeleteThese are the kind of people who bring India down, when it's galloping at 10.1% GDP and has crossed 5 trillion $ ;-) https://www.hindustantimes.com/business-news/economy-in-bad-shape-5-tn-gdp-target-simply-out-of-question-ex-rbi-governor-c-rangarajan/story-YC4CglgchqN0UfQrIEMwmO.html
Captain, please see the disgusting love-jihad cases.
ReplyDeletehttps://www.facebook.com/rati.hegde/posts/2806989462665952
Sent to trump and putin from their official websites.
ReplyDeleteTweeted to Iranian embassy-
https://mobile.twitter.com/Chn_123/status/1197736723007934464
Recently, there was an AI event in our office Captain. I was part of the quiz team and your series on AI was very helpful. I covered the ethical aspects of AI including lack of conscience and new scenario creation given a set of parameters. Not many people had understood to what extent AI can be misused. We had a very open interactive session. Thank you very much.
ReplyDeleteRegards,
Prapulla
Ajit Garu,
ReplyDeletein twitter suddenly the rainbow has become the symbol of LGB ppl. I had a spat with Ashok Row Kavi @Amma29, then suddenly many hindu bearing names came in support of this guy. One of them has Sri Rama in his profile pic @bhrgvr. I think many Hindus are being conditioned by the MSM and also Fringe Hindu groups that LGB is a good thing. i think there is a very big LGB lobby in mumbai sponsoring these ppl. And we have the BJP star Kid @Tejasvi_Surya supporting it
https://twitter.com/adityasbharat/status/1197552236194172928
Namaskaram,
Ravi
Ehat to talk if RSS, even Samiti (ladies counterpart of sangh) has several 377 cases
DeleteTejasvi Surya got MP ticket because he supports LGBT community.
Deletehttps://en.wikipedia.org/wiki/Nowhera_Shaik
ReplyDeleteNOWHERA SHAIK IS B TEAM OF BJP AS PER TOP COPS..
HER HUGE POSTERS SUPPORTING MODIs SWATCH BHARAT ETC HAVE BEEN DISPLAYED ALL OVER BANGALORE BEFORE THE KARNATAKA ASSEMBLY ELECTIONS..
I HAD GIVEN SIX SITREPS IN MY BLOGS ( COMMENTS ) ABOUT THIS WOMAN , STAYING IN THE KINGS SUITE OF A SEVEN STAR HOTEL OF BANGALORE AND RUNNING UP A HOTEL STAY BILL IN CRORES..
I REPORTED TO ALL AND SUNDRY INCLUDING CEC/ EC / PM/ PRESIDENT / CM ETC THAT THIS WOMAN DISTRIBUTED HUNDREDS OF MOBILE PHONES TO POOR MUSLIM VOTERS FROM HER LUXURY HOTEL ROOM..
HUNDREDS OF POOR PEOPLE WEARING SHABBY CLOTHES AND CHEAP PLASTIC CHAPPALS STREAMED INTO HER ROOM FOR MONTHS..
I HAD WARNED SEVERAL TIMES THAT VOTERS MUST NOT BE ALLOWED TO TAKE A MOBILE PHONE WITH CAMERA INTO THE POLLING BOOTH.. YET NOTHING HAPPENED ..
##############
I ASK MY READERS TO WATCH THE NETFLIX SERIAL "EL CHAPO" SEASON 3 , EPISODE 5 ..
DRUG LORD EL CHAPO CHOOSES HIS OWN SLAVE MEXICAN PRESIDENT BY BRIBING VOTERS..
HIS GANG DISTRIBUTED THOUSANDS OF MOBILE PHONES WITH CAMERA TO POOR VOTERS .. ALL THEY HAD TO DO WAS TO PRESS A BUTTON WHEN THEY VOTED USING A CROSS MARK..
THEN THESE BRIBED VOTERS COULD GO TO ANY SAFE HOUSE AND COLLECT SHOPPING COUPONS , WHICH COULD BE REDEEMED IN SELECTED SHOPPING MALLS OWNED BY EL CHAPO..
PEOPLE STILL WONDER HOW KACHRAWAAL WON 4 LOK SABHA SEATS IN DRUG CONSUMING PUNJAB IN THE PREVIOUS ELECTIONS WHEN MODI FIRST BECAME PM..
WE THE PEOPLE WATCH..
capt ajit vadakayil
..
PUT ABOVE COMMENTS IN WEBSITES OF --
DeleteINDIAN AMBASSADOR TO MEXICO
EXTERNAL AFFAIRS MINISTER/ MINISTRY
TRUMP
PUTIN
CEC
EC
PMO
PM MODI
AJIT DOVAL
RAW
IB
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ALL THREE ARMED FORCE CHIEFS.
RBI
RBI GOVERNOR
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NCERT
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ROMILA THAPAR
RAJEEV CHANDRASHEKHAR
MOHANDAS PAI
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AMITABH KANT
RAM MADHAV
RAJ THACKREY
UDDHAV THACKREY
VIVEK OBEROI
GAUTAM GAMBHIR
ASHOK PANDIT
ANUPAM KHER
KANGANA RANAUT
VIVEK AGNIHOTRI
KIRON KHER
MEENAKSHI LEKHI
SMRITI IRANI
PRASOON JOSHI
MADHUR BHANDARKAR
SWAPAN DASGUPTA
SONAL MANSINGH
MADHU KISHWAR
SUDHIR CHAUDHARY
GEN GD BAKSHI
SAMBIT PATRA
RSN SINGH
SWAMY
RAJIV MALHOTRA
THE QUINT
THE SCROLL
THE WIRE
THE PRINT
MK VENU
MADHU TREHAN
CLOSET COMMIE ARNAB GOSWMI
RAJDEEP SARDESAI
PAAGALIKA GHOSE
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NIDHI RAZDAN
FAYE DSOUZA
RAVISH KUMAR
PRANNOY JAMES ROY
AROON PURIE
VINEET JAIN
RAGHAV BAHL
SEEMA CHISTI
DILEEP PADGOANKAR
VIR SANGHVI
KARAN THAPAR
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N RAM
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I&B DEPT/ MINISTER
LAW MINISTER/ MINISTRY
ALL CONGRESS SPOKESMEN
RAHUL GANDHI
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QUORA CEO ANGELO D ADAMS
QUORA MODERATION TEAM
KURT OF QUORA
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KONRAED ELST
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RANA AYYUB
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PRAKASH RAJ
KAMALA HASSAN
ANNIE RAJA
JOHN BRITTAS
ADOOR GOPALAKRISHNAN
ROMILA THAPAR
SADGURU JAGGI VASUDEV
SRI SRI RAVISHANKAR
BABA RAMDEV
PAVAN VARMA
RAMACHANDRA GUHA
JOHN DAYAL
KANCHA ILIAH
FATHER CEDRIC PERIERA
ANNA VETTICKAD
FAZAL GHAFOOR ( MES KERALA)
MAMMOOTY
DULQER SALMAN
IRFAN HABIB
NIVEDITA MEMON
AYESHA KIDWAI
VC OF JNU/ DU/ JU / TISS / FTII
ALL SOCIAL SCIENCES PROFESSORS OF JNU/ DU/ JU / TISS
SWARA BHASKAR
IRA BHASKAR
ROHINI CHATTERJEE
PINARAYI VIJAYAN
KODIYERI BALAKRISHNAN
PRAKASH KARAT
BRINDA KARAT
SITARAM YECHURY
SUMEET CHOPRA
DINESH VARSHNEY
BINAYAK SEN
SUDHEENDRA KULKARN
D RAJA
ANNIE RAJA
WEBSITES OF DESH BHAKT LEADERS
SPREAD OF SOCIAL MEDIA
Tweeted to -
Delete@SpokespersonECI @ECISVEEP @PMOIndia @narendramodi @HMOIndia @IndEmbMexico @EmbaMexInd @IndianDiplomacy @indiandiplomats @DrSJaishankar @MEAIndia @Ajit_Doval @dir_ed @RAWHeadOffice @NIA_India @nib_india @EACtoPM @NITIAayog @LiveLawIndia @barandbench
@CMO_Odisha @CMofKarnataka @AndhraPradeshCM @ChhattisgarhCMO @CMOfficeUP @TelanganaCMO @MamataOfficial @CMMadhyaPradesh @VasundharaBJP @NitishKumar @Dev_Fadnavis @neiphiu_rio @sarbanandsonwal @PemaKhanduBJP @ArvindKejriwal @CMOTamilNadu @ncbn @CMOKerala @tarun_gogoi @RajCMO @capt_amarinder @RajGovOfficial @Naveen_Odisha @virbhadrasingh @ysjagan @anandibenpatel @drramansingh @yadavakhilesh @SangmaConrad @vijayanpinarayi @PemaKhanduBJP @bhupeshbaghel @DrPramodPSawant @vijayrupanibjp @mlkhattar @jairamthakurbjp @dasraghubar @BSYBJP @vijayanpinarayi @NBirenSingh @ZoramthangaCM @OfficeOfKNath @vijayrupanibjp
THERE IS A LATEST TREND IN SABARIMALA..
ReplyDeleteTHE DISEASE OF MECCA HAS NOW INFILTRATED SABARIMALA PILGRIMAGE.. GANGS OF TAMIL BEGGARS LINE THE ROADS TO SABARIMALA..
THIS MUST BE STOPPED.. BEGGARS MUST BE IMPRISONED FOR MINIMUM ONE MONTH...
PILGRIMS MUST NOT BE HARASSED..
IN KERALA NO HINDU DEVOTEES ARE HARASSED LIKE IN NORTH INDIA WHERE GOON PANDAS ( TEMPLE PRIESTS ) MAKE LIFE MISERABLE FOR DEVOTEES..
capt ajit vadakayil
...
PUT ABOVE COMMENT IN WEBSITES OF--
DeletePMO
PM MODI
AJIT DOVAL
RAW
IB
NIA
ED
CBI
AMIT SHAH
HOME MINISTRY
NEW CJI
ALL SUPREME COURT JUDGES
ATTORNEY GENERAL
CM OF KERALA PINARAYI VIJAYAN
DGP OF KERALA
COLLECTOR OF PATHANAMTHITTA
DEVASWOM BOARD PRESIDENT
DEVASWOM MINISTER
KERALA HIGH COURT CHIEF JUSTICE
Sent to-
Deletedcpta.ker@nic.in
presidenttdb@gmail.com
secretarytdbtvm@gmail.com
chiefminister@kerala.gov.in
kadakampallysurendran99@gmail.com
sent thru mail to
Deletecontact@amitshah.co.in
https://twitter.com/DvSathvik/status/1197891339347607552
https://twitter.com/DvSathvik/status/1197891390425812992
https://twitter.com/DvSathvik/status/1197895723179597825
https://twitter.com/DvSathvik/status/1197895782394785798
Sent emails and tweeted to Kerala handles.
DeleteCaptain, message sent via email to Executive Officer, Sabarimala (eosabarimala@gmail.com), Superintendent of Police (sptdbvig@gmail.com), Devaswom Commissioner(dcotdb@gmail.com) and secretary of devaswom (secretarytdbtvm@gmail.com). I have asked for an acknowledgement.
DeleteSent to
Deletedcpta.ker@nic.in
presidenttdb@gmail.com
kadakampallysurendran99@gmail.com
chiefminister@kerala.gov.in
hckerala@nic.in
dgp.pol@kerala.gov.in
jscpg-mha@nic.in
alokmittal.nia@gov.in
FAYE DSOUZAs ANTI-HINDU ROLE HAS NOW BEEN USURPED BY A FEMALE JOURNALIST NAMED SAHAR ZAMAN ON MIRROR NOW TV..
ReplyDeletehttps://en.wikipedia.org/wiki/Sahar_Zaman_(journalist)
WE THE PEOPLE WARN HER.. YOU ARE BEING MONITORED...
WE THE PEOPLE PUT PRAKASH JAVEDEKAR ON NOTICE.. WHY DO YOU ALLOW BENAMI MEDIA TO DO ENDLESS RABBLE ROUSING..
WHY IS NEWS DELIVERED WITH QUESTION MARK AT THE END OF A SENTENCE?
NEWS MUST BE STATEMENT OF FACTS NOT SPECULATION .. ONLY THE EDITOR IS ALLOWED OPINIONS..
capt ajit vadakayil
..
PUT ABOVE COMMENT IN WEBSITESOF--
SAHAR ZAMAN
I&B MINISTER/ MINISTRY
PMO
PM MODI
AJIT DOVAL
RAW
IB\NA
ED
CBI
AMIT SHAH
HOME MINISTRY
MIDGET VINEET JAIN
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ALL SUPREME COURT JUDGES
ATTORNEY GENERAL
LAW MINISTER/ MINISTRY
RSS
VHP
AVBP
DAVID FRAWLEY
STEPHEN KNAPP
WILLIAM DALRYMPLE
KONRAED ELST
FRANCOIS GAUTIER
RAM MADHAV
RAJ THACKREY
UDDHAV THACKREY
VIVEK OBEROI
GAUTAM GAMBHIR
ASHOK PANDIT
ANUPAM KHER
KANGANA RANAUT
VIVEK AGNIHOTRI
KIRON KHER
MEENAKSHI LEKHI
SMRITI IRANI
PRASOON JOSHI
MADHUR BHANDARKAR
SWAPAN DASGUPTA
SONAL MANSINGH
MADHU KISHWAR
SUDHIR CHAUDHARY
GEN GD BAKSHI
SAMBIT PATRA
RSN SINGH
SWAMY
RAJIV MALHOTRA
I ASK MY READERS TO GET A RESPONSE FROM SAHAR ZAMAN..AND CONVEY IT TO ME..
Deletedear captain, sent the above message through her personal website. got a template response. waiting for her reply in mail. "Thank you for contacting me. I'll get back to you shortly. Regards, Sahar Zaman"
Deletehttps://twitter.com/rakeshsivan/status/1197824969020850181
Deletehttps://twitter.com/rakeshsivan/status/1197824522788859904
Captain sir,
Deletesent her tweet, will see if she replies.
https://twitter.com/shree1082002/status/1197855933612863489
DeleteSahar Zaman SM profiles..
Deletehttps://www.facebook.com/zaman.sahar/
@saharzaman
https://twitter.com/DvSathvik/status/1197890118800957440
Deletehttps://twitter.com/DvSathvik/status/1197890012429242369
also sent thru her website..
Capt, have sent your message to her via http://www.saharzaman.com/contact-me.php
DeleteTweeted
DeleteRamash
@Ramash53607284
·
4m
@saharzaman , we the people watch all anti sanatan dharam agents. Pls don't be one of them
Tagged her on Instagram and Facebook and commented publicly on her accounts.. Asking to answer..
DeleteSent your message to her twitter site and her website.
DeleteGot the automated response below.
Thank you for contacting me. I'll get back to you shortly.
The POC to expose Faye D'Souza is Savio Rodrigues whose Twitter ID is @PrinceArihan
DeleteSavio Rodrigues is Founder & Editor in Chief of @GoaChronicles.
Very honest man & always stood for the Nation.
He has been threatened by Underworld also.
Next time for exposing Faye D'Souza, the only person is Savio Rodrigues.
Prakash Javedkar is doing nothing.
Literally doing nothing.
We asked Javedkar to impose heavy penalty on Rahul Kanwal but it's not working at all.
There is no hope we have either from Prakash Javedkar or Nirmala Sitharam.
Both are useless individuals from Rajya Sabha back door entry.
Once Arnab Goswami was trying to impress Javedkar by asking how Javedkar maintains such a smile but since Javedkar was busy & was corresponding from his Official government car, he couldn't reply & the moment of mutually admiring eachother could not take off.
Sir,
ReplyDeleteDid put d comment on Sahar Zamans website .Said /wrote she'll be getting back to me shortly. Lets see.
Hari Om
This comment has been removed by the author.
ReplyDeletehttps://factcheck.afp.com/joke-new-york-restaurant-did-not-get-licence-serve-human-flesh
DeleteThe news is fake.
Guruji ,African Nations are rolling out single currency ECO like our beloved Euro.Is this some new gimmick?
ReplyDeleteTrue Indology handle on twitter is operated by a person who can't differentiate right from wrong.
ReplyDeletehttps://twitter.com/TIinExile/status/1197836088804438017?s=20
True Indology believes that Uttar Kanda is legit and that Rama kicked his wife Sita into forest.
Rajiv Malhotra is another Hindu pleaser. He won't dare to dwell into things that Hindus might find offensive.
These babes in the woods really think that Adi Shankaracharya would say 'Worship Govinda, Worship Govinda, Worship Govinda, Oh fool!'
Is that how a Maharishi with pure mind will speak? Or has white man put words into his mouth?
Everyone here including Ajit Sir,
DeleteThat @TlinExile handle is operated by multiple heads not one single individual.
Off late that handle got exposed bcoz that छछून्दर group of heads have no Gratitude.
Sanjay Dixit Sir helped that handle when a fellow beauracrat was threatening to doxx those group of arrogant individuals during 2019 Parliamentary elections.
They were not arrogant then.
Then this fellow who heads the handle started hurling abuses on Sanjay Sir.
It was just 4 days ago.
Pathetic abuses.
Sanjay Sir reciprocated even.
Twitter is a mentally sickening platform when a bunch of naive idiots patronize a particular handle.
This has happened, @TlinExile has got hold of a fan base.
The only thing that handle does is getting some documents highlighted & attaches it with comments & others go Ga Ga.
I got caught up during those abuses getting hurled upon each other.
I supported Sanjay Sir bcoz for me individual effort matters more.
Obviously a group of heads will give more output.
I asked if you are a handle with multiple users at the background.
That handle tagged me up & rest is history.
I got trolled but I gave back to all of his troll.
It was painful, the entire night I had to remain awake.
Anyways Sanjay Sir took notice, atleast he knew that one fellow did come & talk about Morales.
That's not the way of sharing knowledge, getting some documents highlighted & attaching it.
No sensibility, nothing
We readers can find his mistakes bcoz of Ajit Sir else by this time we would also have been kissing that handle.
If Rajiv Malhotra is reading this comments section then I call him to be a big time BASTARD, and I don't regret it.
No one, I reiterate No one in this world has in a granular manner tried to explain his/her fans the way our Ajit Sir has done.
Those on Twitter are only seeking followers by clever manipulation.
They don't have any mission while Ajit Sir has mission statement.
Ajit Sir,
You are the best also the only one unique person.
Free knowledge to everyone.
You are a true philanthropist.
https://timesofindia.indiatimes.com/city/hyderabad/34000-indians-died-in-gulf-in-five-yrs-1200-from-state/articleshow/72175377.cms
ReplyDeleteIN NORTH KERALA AN UNSKILLED LABOURER FROM BIHAR / UP/ BANGLADESH CHARGES 900 RUPEES PER DAY..
HE WILL GET ONLY 40 %..
THE REST GOES TO AGENCY ( BOTH SIDES ) / POLICE
https://edition.cnn.com/2019/11/21/world/nasa-sugar-meteorites-intl-hnk-scli/index.html
ReplyDeleteSHIVA LINGAMS WHICH RAINED INTO NARMADA RIVER CREATED THE FIRST HUMANS 65 MILLION YEARS AGO..
http://ajitvadakayil.blogspot.com/2019/07/shiva-lingam-meterorite-hit-65-million.html
INDEED SHIVA LINGAM IS THE DIVINE PHALLUS WHICH GAVE THE SEED.
http://greatgameindia.com/ctd-advisors-rebuilding-british-empire-of-modern-times/ Shashi Tharoor, a serving member of the Indian Parliament lately known for his anti-British position has ironically been recruited by the son of a Pakistani British spy in his firm CTD Advisors, heavily infested with former British intelligence chiefs advocating foreign intervention in Kashmir and with the objective to rebuild the British Empire of modern times.
ReplyDeletehttps://news.bitcoin.com/indian-supreme-court-wraps-up-crypto-hearing-for-the-year/
ReplyDeleteWE KNOW WHY DEEP STATE DARLING MODI IS PROTECTING BITCOIN..
Private Bill was introduced in the Lok Sabha today by BJP MP Dr. Satyapal Singh to free Hindu temples from govt control.
ReplyDeleteLet us see how the govt & opposition work to facilitate the passage of this bill. Time to see their true colours
The very fact that its a private bill coming from a BJP MP tells you all you need to know about the inclination of central govt. Who likes the intoxicating power diluted?
DeletePrivate bills are rarely passed by parliment.since 1970 no private bill got passed.
DeleteCaptain, look at the picture and hidden agenda....
ReplyDeletehttps://timesofindia.indiatimes.com/india/74-per-cent-of-indias-teenagers-physically-inactive-who/articleshow/72192306.cms
Paadi,
DeleteAll those boys in pic are brahmins hindus who eat vegetarian food
https://www.aninews.in/news/national/general-news/rss-backs-muslim-professor-at-bhus-sanskrit-dept-as-students-call-off-strike20191122230931/
ReplyDeleteRss backs Muslim professor at BHU's Sanskrit department as students call off strike.
SHIV SENA IS DEAD .
ReplyDeleteFELLOW OF ZERO CHARACTER AND INTEGRITY UDDHAV THACKERAY HAS DESTROYED HIS FATHERs LEGACY..
IN CALICUT BEFORE ELECTIONS SHIV SENA POSTERS USED TO SPROUT ALL OVER - " CHANKOOTHA THODE PARAYU, NJAN HINDU AANE " ( DECLARE WITH PRIDE , I AM HINDU )
THIS WHEN SHIV SENA CANDIDATES ARE NOT CONTESTING IN KERALA..
SANJAY RAUT HAD DESTROYED SHIV SENA FROM WITHIN..
UDDHAV TURNED OUT TO BE A NOVICE IN POLITICS
PEOPLE LIKE UDDHAV THACKREY ARE THE REASON BHARATMATA WAS IN CHAINS FOR 800 YEARS..
JAI JAI SHIV SENA
BHARATEEYA SONIA SENA
OOPAR DEKHO MAINO JEW SENA
NEECHE DEHO TO SPAGHETTI SENA
EVERYWHERE DEKHO ITALIAN COLCOTRONI SENA
OPEN OUT ALL CORRUPTION CASES AGAINST SHARAD PAWAR
DeleteENRON CASE
TELGI CASE
PRATHIBHA SHIPPING CASE
CO-OPERATIVE BANK SCAM
SHARAD PAWAR IS INDIAs MOST CUNNING AND SHREWD POLITICIAN.. HE IS PLAYING TO THE GALLERY..
THE PAWAR FAMILY SPLIT STORY IS FOR FOOLS..
But Ajit Sir,
DeleteThis nexus is terribly horrible
I'm suffering from headache from this morning I got up reading the NEWS of BJP-NCP
Supriya Sule will not allow any legal case filed against her father Sharad Pawar.
Sharad Pawar will be silenced & this unholy nexus will continue.
No case is going to get opened up.
This Sharad Pawar will die a natural death like Arun Jaitley & with this all cases will end up in oblivion.
भारत के लोगों को इन्साफ़ नहीं मिलेगा
यही सच्चाई है
Supriya Sule is the same person who criticised openly about the abrogation of ARTICLE370 on Parliamentary floor.
If Shiv Sena has withdrawn the support to BJP in Parliament then it's only a loss of 13 seats(18 seats from Shiv Sena minus 5 seats from NCP) as support.
Hardly matters.
It's a dark day.
Shiv Sena is the worst party ever that exists in Indian Politics.
They ruined almost a dream that was there to convert to reality.
Dawood Ibrahim will never be caught up.
And that fellow Shehzad Jai Hind Twitter handle hugging up to BJP & who also is brother of Tehsheen Poonawala, check his Tweets.
Till date that fellow has not made a single informative tweet.
All tweets are Gaurav Pradhan kind of tweets.
This people are aspiring to take up #BJP in future.
Pathetic
I have asked a fellow to make a call to Kapil Mishra & speak about Gaurav Pradhan.
Gaurav Pradhan is blocking everyone on Twitter.
Ajit Sir,
If you give permission, I can put that link of Twitter here.
I asked a pretty girl to make the tweet against Gaurav Pradhan for pulling attention.
It didn't take off.
Simple Like and RT will pull everyone's attention.
Atleast the readers here can mass RT & Like it.
All big shots are tagged in that tweet.
Gaurav Pradhan is very irritating to look at.
I have developed hatredness for that BSTD.
ऐसे लोग आगे जाके BJP में official member बन जायेंगे तो देश नहीं चल पायेगा
यह लोग देश को बेच खायेंगे
You have a great sense of humor captain 😂😂😂😂
DeleteGuruji,you are right,this whole split story is some political gimmick.BJP has awarded Sharad Pawar Padma award,abd Modi has himself visited Baramati.This was like a well planned scheme.
DeleteDear Capt Ajit sir,
ReplyDeleteLutyens media has never been more shocked than ever with this new Maha twist as Devendra Fadnavis took oath as CM of Maharashtra.
Rajdeep Sardesai was so shocked with this tectonic development of Governor staying back and made early morning arrangements for swearing in...and the chanakya neeti was done during Modi/Pawar meet....Mumbai breathes again after Maha Pralaya...Kalki cleansing effect is ON.
When a person is sworn in a surprise ceremony, as CM, and THEN the public comes to know about it, it tells you something.
DeleteWhen coalitions happen in secret without public knowledge it tells you something.
It is all about power.
Sir
ReplyDeletePlease see this , demonising Kali and ritual Mudiyettu of kerala. They are using Kodungallur Barani as a main thing. Also pls check invading the sacred book. I don't know y government is not banning it.
http://beingdifferentforum.blogspot.com/2014/02/sarah-caldwell-reinterpreting-hindu.html?m=1
Sarah Caldwell is of Columbia University ,only u can nail this.Iam surprised all r trying to demonise hinduism and promoting debauchery.
Also nowadays leftists telling Hindus constructed temples after destroying budhist shrines , including Sabarimala , quoting Jha and Gail Omvedt.
I donot know how these intellectuals will damage our India.
Please expose budhism and such intellectuals,it's more dangerous now.
Thankyou
Dear Capt Ajit sir,
ReplyDeleteSharad Pawar to be next President of India....the way Prez rule ended at 5:30 am today, Governor administering oath to Fadnavis at 8 am...yeh hai digital India...kya timing !!!!
Sir, Created Meme for "Operation hindu revival". Need further development. Please once check it out.. Does this look okay to you? whats app is the best flat form to share memes to reach more.
ReplyDeletehttps://twitter.com/tuki00108/status/1198178481651081217
Gratitude.
SOMEBODY ASKED ME --
ReplyDeleteWHAT IS THE DIFFERENCE BETWEEN PABLO ESCOBAR AND EL CHAPO
AFTER EL CHAPOs EXTRADITION TO USA, AT THE END OF THE INQUIRY THE JUDGE DEEMED THAT EL CHAPO KILLED NEARLY 3000 PEOPLE..
EL CHAPO LED HIS GANG INTO BATTLE LEADING BY RUTHLESS AND VICIOUS EXAMPLE.... PABLO WAS ALWAYS BEHIND THE BATTLE LINES, LIKE THE GENERAL.
EL CHAPO WAS MEAN AND SINISTER..HIS TEAM WERE AFRAID OF HIM.. PABLO WAS LOVED BY HIS TEAM..
MOST OF DEATHS ATTRIBUTED TO PABLO ARE "FALSE FLAG ATTACKS " BY THE JEWISH DEEP STATE WHERE MASS BOMBINGS TO MINDLESSLY KILL PUBLIC WERE PUT INTO HIS ACCOUNT BY THE DEEP STATE AND YELLOW JEWISH MEDIA..
WHEN PABLO WAS IN "CATHEDRAL PRISON" , THEY DID FALSE PROPAGANDA THAT HE CLUBBED TO DEATH A CARTEL PARTNER, BECAUSE HE HAD A BAD ATTITUDE.. THIS WAS A LIE, WHICH MADE OTHER CARTELs WARY OF PARTNERING PABLO..
EL CHAPO HAD THE INDIRECT ASSISTANCE OF MEXICAN PRESIDENT, US PRESIDENT , DEA, CIA , DEEP STATE MEDIA.. THIS IS HOW HE STAYED AT TOP ..
EL CHAPO WAS CHOSEN BY US PRESIDENT AND DEA/ CIA TO HEAD THE MEXICAN CARTELS.. EL CHAPO DECIDED WHO WILL BE THE NEXT MEXICAN PRESIDENT..
EL CHAPO WAS A DARK SINISTER WARLORD , INTERESTED ONLY IN HIS OWN SELFISH METHODS/ MOTIVES ..
PABLO WAS A LEADER WHO WAS PATRIOTIC TO HIS WATAN.. WHO WANTED HIS MOTHERLAND TO BE FREE FROM JEWISH BLOOD SUCKING TENTACLES..
EL CHAPO HAD CIA/ DEA HELP TO EXPAND HIS BUSINESS ( METH/ HEROINE/ COCAINE ) THROUGHOUT THE WORLD..
PABLO DID ONLY COLOMBIA TO USA COCAINE TRADE.. HE DID NOT WANT HIS OWN COUNTRYMEN TO USE COCAINE..
PABLO HAD ONLY ONE WIFE, HE WAS DEDICATED TO HER.. EL CHAPO HAD 4 OFFICIAL WIVES AND DOZENS OF MISTRESSES.. EL CHAPO WAS A RAPIST OF UNDERAGED GIRLS..
capt ajit vadakayil
..
Isn't it strange to now think.back and see that with marijuana getting legalised that most of the deaths coming from the Mexican Juarez and Sinaloa cartel were so eminently avoidable? Even in Pablo's case the market for cocaine addicts in Miami along with the distributors to get the product put would have been necessary for him to ever leave a mark and need the deas attention in the 80s. 80s are still known as the decade of coke addicts and drug kingpins because of this and movies like Scarface which incidentally was based on another notorious 'pusher' of the prohibition era al Capone whom even the other mafia families were wary of.
DeleteI still wonder how the real scene of Felix meeting Pablo went in real life.
The principles of calculus were first laid down not in German or English or even Sanskrit. What a sweet surprise that it was codified in Malayalam! Truly the crowning glory and achievement of the malayalam language. But how many people even in Kerala know this truth? A fraction!
ReplyDeletehttps://twitter.com/JoeAgneya/status/1198099977215471616?s=20
DANAVA CIVILIZATION DABBLED IN ADVANCED MATH / CALCULUS EVEN BEFORE THE VEDIC CIVILIZATION WAS BORN..
Deletehttp://ajitvadakayil.blogspot.com/2011/01/isaac-newton-calculus-thief-capt-ajit.html
KODUNGALLUR UNIVERISTY WAS THE FIRST UNIVERSITY ON THE PLANET..
SECOND UNIVERSITY TAXILA CAME SEVERAL MILLENNIUMS LATER WITH NAMBOODIRI PROFESSORS FROM KODUNGALLUR UNIVESITY..
FOREIGN UNIVERSITIES IN GREECE/ EGYPT ETC SPROUTED 2600 YEARS AGO, WITH PROFESSORS FROM KODUNGALLUR..
THERE WERE MALAYALI FEMALE PROFESSORS TOO IN FOREIGN UNIVERSITIES.. HYPATIA WAS A MATH PROFESSOR..
I HAVE DESCRIBED HER IN THE POST BELOW-- SHE WAS TREATED AS A GODDESS WHEN ALIVE..
http://ajitvadakayil.blogspot.com/2019/07/secrets-of-12000-year-old-machu-picchu_30.html
Sharing within Joseph Noony's Tweet:
Deletehttps://twitter.com/AghastHere/status/1198481165989990401?s=20
SOMEBODY ASKED ME ABOUT JEWISH DEEP STATE AND EL CHAPO
ReplyDeleteYOU WILL NEVER GET THIS IN THE MAIN STREAM MEDIA..
THE ENTIRE WORLD MAFIA IS JEWISH..
JEWS HAVE MONOPOLIZED DRUGS/ PROSTITUTION/ GAMBLING / CRIME / EXTORTION MAFIA FOR CENTURIES ..
SICILIAN MAFIA IS JEWISH ( GODFATHER MOVIE )
IT IS GOOD THAT NETFLIX IS SHOWING MAFIA TV SERIES..
THE NETFLIX TV SERIAL"PEAKY BLINDERS" SHOW JEWISH GANGS ..THEY WERE CONTROLLED BY JEW CHURCHILL DURING WW1 AND WW2.
ROTHSCHILD CONTROLLED MAIN STREAM MEDIA WILL EVER TALK ABOUT JEWISH GANGS-- BECAUSE JEW ROTHSCHILD IS GANGSTER NO 1
https://listverse.com/2016/09/25/top-10-jewish-crime-syndicates/
https://twitter.com/AghastHere/status/1198479519859953664?s=20
DeleteMore twists and turns in Maharashtra politics. A streetwalker could learn from them . Nephew proposes uncle disposes..
ReplyDeleteThe understanding with Shiv Sena for Sonia was there even before the seat sharing talks. But still the congress asked Uddhav to win few MLAs on the back of BJP. Raj Thackeray knew it. This is the dirtiest trick ever played by the Italian Mafia. This is cheating the peoples mandate.
ReplyDeletehttps://timesofindia.indiatimes.com/city/mumbai/raj-thackeray-predicted-shiv-sena-bjp-wont-form-maharashtra-government/articleshow/72193585.cms
Dear Captain,
ReplyDeleteEven I work on Cloud Computing projects. We build solutions for different industries on salesforce.com platform. Recently big event of salesforce was conducted in San Francisco, called DreamForce, Barak Obama came to lecture engineers, along with champ Mark benioff Chairman and founder of Salesforce.com.
This dude acknowledges a spiritual Guru name "Paramhansa Yogananda" for being his spiritual advisor. He even said to have came to India to heal himself and to start his new venture "Salesforce.Com". He and his wife both American Jewish, involved in huge charity works there.
Salesforce.com has grown huge over last decade. from desktop oriented cloud based application to today's AI capable Force.com platform, capable of catering to needs of any industry. He has done big acquisitions to include products like Marketing Cloud, mobile based app called salesforce 1, Salesforce Outlook etc. I suspect why any other player is not venturing in this parlance of industry? There is no competition to this dude since over a decade. Though tool is marketed as best of the market, being a developer and part of implementation team, we often run in issues and have to raise case to salesforce support teams. Despite that market is going gaga about this and everyone is running behind salesforce.
I feel it is not due to talent or quality of product but uber rich Jewish lobby supporting this, what is your stake Sir?
TIME MAGAZINE BELONGS TO JEW MARC BENIOFF FOUNDER OF CLOUD COMPUTING COMPANY SALESFORCE..
DeleteTHERE ARE NO SECRETS IN CLOUD - JEWISH DEEP STATE CONTROLS IT..
how to share your post on fb ?
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1.Copy the link and paste it for the full post to be read.
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Tiny url
Deletehttps://timesofindia.indiatimes.com/india/no-cabinet-meet-pm-uses-powers-to-revoke-article-356/articleshow/72204459.cms
ReplyDeletehttps://indiankanoon.org/doc/8019/
TILL TODAY NOT ONE SINGLE COLLEGIUM JUDGE HAS BEEN ABLE TO UNDERSTAND THE INDIAN CONSTITUTION..
THE INDIAN CONSTITUTION IS A DIRECT LIFT OF THE BRITISH CONSTITUTION WRITTEN BY JEW ROTHSCHILD-- WITH SOME ADDITIONAL SOCIAL ENGINEERING CLAUSES WRITTEN BY BR AMBEDKAR ON DIRECTIONS OF COMMIE JEW JOHN DEWEY..
THE JUDICIARY HAS NOT UNDERSTOOD THAT THE INDIAN CONSTITUTION PROVIDES SUBJECTIVE VETO POWERS AND SUBJECTIVE DISCRETIONARY POWERS TO THE STATE GOVERNORS AND INDIAN PRESIDENT.. REST ARE ALL GIVEN ONLY "OBJECTIVE" POWERS ..
LIKE I SAID-- WHEN A SHIP IS VETTED BY AN OIL MAJOR , THERE WILL BE 999 OBJECTIVE QUESTIONS.. THE SHIP CAN PASS ALL THE 999 "OBJECTIVE" QUESTIONS..
THE FINAL "SUBJECTIVE" QUESTION TO THE VETTING INSPECTOR WILL BE " ARE YOU WILLING TO SAIL ON THIS SHIP WITHOUT RESERVATION FOR A VOYAGE NUDER THE PRESENT CAPTAIN AND CREW"..
IS THIS ANSWER I NEGATIVE , THE SHIP HAS FAILED..
THE JUDICIARY HAS NO POWERS TO CURB THE SUBJECTIVE POWERS VESTED IN THE PRESIDENT/ STATE GOVERNORS..
THE CAPTAIN OF A SHIP HAS "SUBJECTIVE" POWERS.. I MADE SURE THIS HAPPENED.. SUBCONSCIOUS BRAIN LOBE AND GUT FEELINGS ARE EVOKED HERE FOR A FINAL DECISION..
ARTIFICIAL INTELLIGENCE CAN NEVER INVOKE THE "SUBJECTIVE"..
THIS IS MY TIMELESS GIFT TO THE SEA.
https://ajitvadakayil.blogspot.com/2019/10/when-there-is-danger-of-his-ship.html
capt ajit vadakayil
..
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Your Registration Number is : PMOPG/E/2019/0674149
DeleteSOME LAWYER ASKED ME
ReplyDeleteCAPTAIN ARE YOU THE ONLY WISE GUT WHO CAN WRITE LEGACIES OF IMPRTANT PEOPLE? YOU WROTE THAT YOU WILL WRITE THE LEGACY OF SUPREME COURT JUDGES CHANDRACHUD/ NARIMAN..
LISTEN--
TO WRITE LEGACIES YOU MUST BE ABLE TO DO 4D VADAKAYIL BINDU THINKING..
4D BECAUSE - I INCLUDE "TIME AXIS"..
ROTHSCHILD COULD MAKE AN ASS OF THE WHOLE PLANET BECAUSE HE COULD CHEAT ON THE TIME AXIS WITHOUT GETTING CAUGHT..
WHAT IS TIME AXIS ?
READ ABOUT TWO CHESS PLAYERS ON A CRUISE SHIP IN THE POST BELOW--
http://ajitvadakayil.blogspot.com/2013/06/travesty-of-justice-sreesanth-and.html
YOU SHOULD BE ABLE TO SIT AT THE BINDU ( CORE ) AND LOOK OUTWARDS.. YOU SHOULD BE ABLE TO TRIANGULATE..
YOU LEAVE LEGACY "IN" PEOPLE -- NOT "FOR" PEOPLE.. IT IS ABOUT LEAVING YOUR INDELIBLE FOOT PRINTS ON THE SANDS OF TIME.. IMMORTALITY..
I LEFT A LEGACY AT SEA.. THIS IS THE STUFF OF LIVING LEGENDS.. THE NAME IS CARVED ON SOULS NOT TOMB STONES.
EXAMPLE?
WHAT WAS THE LEGACY OF PABLO ESCOBAR / EL CHAPO..
ONE WAS A MAN-- THE OTHER WAS A MOUSE..
PABLO WENT IN A BLAZE OF GLORY.. HE HAD WRITTEN TO THE MEXICAN PRESIDENT " I PREFER A COFFIN IN MY BELOVED HOMELAND - NOT A PRISON CELL IN ALIEN USA"
EL CHAPO IS ROTTING IN A US PRISON .. HE WILL BE THERE FOR 30 YEARS..
BLAZE THAT TRIAL-- ALWAYS !
https://www.youtube.com/watch?v=8J4uqngyA4U
ARE YOU A MAN -- OR A MOUSE ?
THE WORLD IS FULL OF GUYS AND DUDES -- BE A MAN !
capt ajit vadakayil
..
Hello Sir,
Deleteon a different topic, has rock n roll and rock music died ?
has the jewish monopoly on media killed good entertainment and music ?
https://en.wikipedia.org/wiki/Peaky_Blinders_(TV_series)
ReplyDeletehttps://en.wikipedia.org/wiki/Peaky_Blinders
I WANT THE STUPID JOHN BULLS AND LIMEYS GET THESE TRUTHS CLEAR..
"PEAKY BLINDERS" DID NOT HAVE RAZORS SEWED INTO THEIR PEAK CAPS TO BLIND THEIR OPPONENTS..
THE HERO THOMAS SHELBY WAS A ROMANI GYPSY.. GYPSIES ARE OF MIXED INDIAN BLOOD.. THEIR PROGENITORS WERE BEAUTIFUL INDIAN DANCERS WHO WERE KIDNAPPED FROM SOMNATH TEMPLE BY INVADING MUSLIMS..
http://ajitvadakayil.blogspot.com/2013/11/the-sack-of-somnath-temple-by-mahmud-of.html
THE LEE GANG ARE NOT CHINESE , BUT A RIVAL GYPSY GANG WHO SPOKE ROMANI LANGUAGE.. YOUNGEST BROTHER JOHN SHELBY WOULD LATER MARRY A LEE GIRL..
JEW ROTHSCHILD CREATED COMMUNISM.. BOLSHEVIK REVOLUTION WERE DONE BY JEWS..
http://ajitvadakayil.blogspot.com/2013/07/exhuming-dirty-secrets-of-holodomor.html
IRISH WERE TREATED LIKE SHIT AND SHIPPED OFF ABROAD AS WHITE SLAVES AND ALSO TO AMERICA BY JEW ROTHSCHILD..
THE POTATO BLIGHT FAMINE WAS DELIBERATELY DONE BY JEW ROTHSCHILD, TO FORCE IRISH TO AMERICA..
http://ajitvadakayil.blogspot.com/2015/02/the-charge-of-light-brigade-exhuming.html
ROTHSCHILD DELIBERATELY CREATED FAMINE IN INDIA,TO SHIP OUT ENTIRE INDIAN FAMILIES AS SLAVES..
WE ARE TAUGHT ROTHSCHILDs FALSE HISTORY IN OUR NCERT BOOKS.. ROTHSCHILD CHOOSES OUR EDUCATION MINISTERS..
http://ajitvadakayil.blogspot.com/2010/04/indentured-coolie-slavery-reinvented.html
THIS IS HOW IRA CAME UP, TO FIGHT WITH ROTHSCHILDs PROTESTANTS AND JEWISH MAFIA..
http://ajitvadakayil.blogspot.com/2015/03/protestant-christianity-created-by-jews.html
JEW WINSTON CHURCHILL WAS AN AGENT OF JEW ROTHSCHILD..
http://ajitvadakayil.blogspot.com/2011/07/winston-churchill-henchman-or-hero-capt.html
ROTHSCHILD SHIPPED BRITISH MADE / US DESIGNED GAS OPERATED 97 ROUND LEWIS MACHINE GUNS TO LIBYA TO CONVERT LIBYAN RULERS TO JEWISH.. THE ROYALTY REMAINED JEWISH TILLED HERO GADDAFI OUSTED JEW KING IDRIS..
http://ajitvadakayil.blogspot.com/2011/10/ethnic-cleansing-of-blackskinned-people.html
ROTHSCHILD RULED INDIA -NOT THE GERMAN JEWISH BRITISH ROYALTY OR THE BRITISH PARLIAMENT..
http://ajitvadakayil.blogspot.com/2018/02/britannia-did-not-rule-waves-and-this.html
INDIANS FOUGHT IN THE TRENCHES WITH SWORDS AND SPEARS IN FRANCE / BELGIUM DURING WW2-- NOT THE COWARDLY ENGLISH WHITE MAN.. THERE IS NO NEED TO IMPRESS US THAT THOMAS SHELBY SMOKED OPIUM TO SLEEP, BECAUSE HE HAS PTSD..
PEAKY BLINDERS IS ABOUT THOMAS SHELBY STEALING THE LEWIS GUNS FROM BA FACTORY --WHICH ROTHSCHILD DID NOT WANT TO LAND IN THE HANDS OF IRA..
JEWISH MAFIA DID EXTORTION AT THE BRITISH RACES .. ALL MAJOR MAFIAS THROUGHOUT THE PLANET ARE JEWISH ..
POOR JOHN BULLS - ALWAYS IN THE DARK LIKE MUSHROOMS --
THEY STILL THINK THEY FOUGHT TWO WORLD WARS FOR QUEEN AND COUNTRY... TEE HEEEEEEE.. IN REALITY THEY FOUGHT FOR JEW ROTHSCHILD, SO THAT HE CAN CARVE OUT ISRAEL..
http://ajitvadakayil.blogspot.com/2010/11/great-romance-and-curse-of-kohinoor.html
capt ajit vadakayil
..
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DeleteDear Capt Ajit sir,
ReplyDeleteThis is true as Ram is revered across ASEAN...and you said it clearly Korea Kim's dynasty roots are in Ayodhya :-)
https://www.thetruepicture.org/ram-temple-in-ayodhya-sout-east-asia-tourism-civilization/
https://m.timesofindia.com/videos/entertainment/hindi/nysa-devgan-becomes-victim-of-online-trolling-yet-again-netizens-criticize-her-for-wearing-midriff-flaunting-crop-top-at-temple/videoshow/72179265.cms
ReplyDeleteYe din dekhne pd rahe hai. Woke culture is In.
Ajit Sir and Readers,
ReplyDeleteKindly have your attention plzzz
TIMES NOW SUPER EXCLUSIVE
Astrophysicist @neiltyson describes humanity’s greatest quest, God, humans, aliens & space on INDIA UPFRONT SPECIAL EDITION with Rahul Shivshankar.
Tune in to TIMES NOW at 12 noon
This is a must watch for all the readers.
Astrophysicist will talk about all these & such a timeline that when our Ajit Sir dug up deep inside to tell how a new set of life started after meteorite strike and how the reduction of Oxygen percentage in atmosphere was the game changer.
I suspect this Astro Physicist for sure might have plagiarized from Ajit Sir.
Everyone plz listen and do figure out what this fellow says.
Thank you!
YOU CAN RESUSCICATE TIRED PEOPLE BY OXYGEN..
DeleteBUT YOU CANNOT USE OXYGEN FOR TOO LONG.. OUR BODY NEED NITROGEN..
INHALED AIR IS BY VOLUME 78.08% NITROGEN, 20.95% OXYGEN AND SMALL AMOUNTS INCLUDE ARGON, CARBON DIOXIDE, NEON, HELIUM, AND HYDROGEN..
EXHALED AIR CONTAINS APPROX -- 6% WATER VAPOR, 74.4 % NITROGEN, 4 % OXYGEN, 4.6 % CARBON DIOXIDE
THE AIR WE BREATHE IS AROUND 78% NITROGEN, SO IT IS OBVIOUS THAT IT ENTERS OUR BODY WITH EVERY BREATH. THIS NITROGEN HELPS IN PROTEIN SYNTHESIS, AMINO ACIDS THAT INFLUENCE GROWTH, HORMONES, BRAIN FUNCTIONS AND THE IMMUNE SYSTEM
65 MILLION YEARS AGO, A SHIVA LINGAM IMPACT, GAVE 78% NITROGEN TO THIS PLANET.. THE DECCAN PLATEAU BURNED FOR 30,000 YEARS.
http://ajitvadakayil.blogspot.com/2019/07/shiva-lingam-meterorite-hit-65-million.html
TRIPLE SRI PLAYED AROUND WITH NITROGEN TO MAKE AN ASS OF THE WHOLE WORLD
http://ajitvadakayil.blogspot.com/2016/09/hyperventilation-followed-by-valsalva.html
capt ajit vadakayil
..
Dear Capt Ajit sir,
DeleteThis is the second biggest revelation in this blogpost of yours after Theertham for me....so you mean to say that when you trek to Mt Everest, you should also have N2 cylinder to breathe in N2 in proportion with O2 for some period in gaps of Only O2 breathing ?
Will this enhance body strength and ensure most deaths are avoided in the last stage of reaching the peak ? Arunima Sinha - amputee climber had come to IBM and shared her story of climbing Mt Kailash and infact because one person was dead on the way up, she used that person's O2 cylinder as hers was empty, because of double usage due to her amputee legs...will N2-O2 combo cylcinder usage help more climbers to reach the top ?
Captain, when an A2-humped-cow dies, should it be buried or cremated ?
ReplyDeleteBURIAL OF LARGE ANIMALS POLLUTES THE GROUND WATER..
DeleteThe Jew DNA definitely has much stronger tribal loyalty than other races. How else can they rely on their own ppl to carry out the R agenda - or is it just straight money calculation?
ReplyDeleteJEWS WANT EASY MONEY BY DECEIT
DeleteTHEY WONT WORK HARD AND HONEST.. THIS IS A DNA PROGRAM CORRUPTION THINGY..
Captain, I noticed this strange-pattern,
ReplyDelete1) Vegan :-- A person who eats vegetables but no dairy
2) Vegetarian :-- A person who eats vegetables and dairy
3) Eggetarian :--- A person who eats vegatables, dairy and eggs (usually chicken)
4) Non-vegetarian :--- A person who eats veg, dairy, eggs and non-human meat
-----------------
So now logically the next higher step would be,
5) Humanitarian :--- A person who eats all of above and human-meat.
-----------------
So is it right to assume that "Humanitarian" is indeed a double-meaning word like that movie-name "Andheri-raat-mein, diya-tere-haat-mein" because everytime there has been a humanitarian-agenda it has always lead to destruction/poisoning/etc. of a region/nation's human-population as we have seen across the world ? In the name of doing-good a lot of bad is done in the name of humanitarian which shows that the propaganda spreads good-meaning of humanitarian but actual-actions are following the destruction-meaning of humanitarian (as above mentioned).
Also, the dark-age Europeans can be ridiculed by Indians by calling them Humanitarian, because they used to be cannibals in their old-ages but are now preaching the "good" meaning of Humanitarian even though it is a hypocritical-vested-agenda preaching.
Who were called rakshas / demon / narbhakshi in old days ? They could be European cannibals only.
DeleteBtw, this fake news have been circulating around facebook since 2016 -
https://empirenews.net/new-york-city-restaurant-becomes-first-to-get-license-to-serve-human-flesh/
SEE THE VIDEO BELOW--
Deletehttps://www.youtube.com/watch?v=uuxeAbeDcU0
JEWS HAVE MONOPOLIZED SERIAL KILLING AND CHRONS ASSHOLE DUE TO THIS..
IMAGINE PAPA CHID HAS CHRONS..
https://www.google.com/amp/s/www.timesnownews.com/amp/sports/cricket/article/ambati-rayudu-is-a-frustrated-cricketer-says-mohammad-azharuddin-on-claims-of-corruption-in-hca/518917
ReplyDeleteAMBATI RAYUDU HAS THE SUPPORT OF CAPT AJIT VADAKAYIL
DeleteAMBATI RAYUDU IS MORE TALENTED THAN SACHIN TENDULKAR.. HE WAS KEPT OUT OF THE INDIAN TEAM BY BCCI , BECAUSE HE JOINED ICL ..
AMBATI RAYUDU WAS CAPTAIN OF INDIAN UNDER 17 TEAM..
NONE OF THE CURRENT BCCI SELECTORS HAVE ANY INTERNATIONAL EXPERIENCE..
JUST WHO THE HELL IS SELECTOR MSK PRASAD..
AZARUDDINs ODI AVERAGE IS 36, WHILE AMBATI RAHUDUs AVERAGE IS 47..
SAME WAY VINOD KAMBLI WAS KEPT OUT OF THE INDIAN TEAM..
KAMBLI AND KOHLI HAVE THE SAME TEST AVERAGE.. THIS DESPITE RECENTLY KOHLI SCORED A LOT OF TEST CENTURIES --SOME UNBEATEN..
http://stats.espncricinfo.com/ci/content/records/282910.html
WE EXPECT SAURAV GANGULI TO PUNISH THE CORRUPT BCCI. THERE IS THOUSANDS OF CRORES INVOLVED..
capt ajit vadakayil
..
Already tweeted to Sourav Ganguly and Cc to Rajeev Chandrasekhar below Republic TV tweets.
Deletehttps://timesofindia.indiatimes.com/home/sunday-times/nithyananda-making-cows-talk-industrialists-open-wallets-and-gopikas-dance-to-his-tune/articleshow/72201844.cms
ReplyDeleteNONE IN THE BENAMI MEDIA HAD ANY PROBLEMS WHEN THE BROTHER IN LAW OF ANDHRA CM JAGAN MOHAN REDDY , A CHRISTIAN EVANGELIST ( SPECIALIZING IN CONVERTING HINDUS ) DECLARED THAT HE CAN START AND STOP THE RAIN..
https://www.youtube.com/watch?time_continue=1&v=A3ORZqWVwKk
LAST WEEK THE ENTIRE BENAMI MEDIA WERE SHOWING NITYANANDA WIPING HIS FOREHEAD AND FLINGING THE SWEAT IT INTO THE HUGE AUDIENCE ..
THIS IS A LOST ART OF KALARI..
WHEN A KALARI WARRIOR IS KNOCKED OUT UNCONSCIOUS, THE GURIKKAL , WHO IS AN EXPERT ON AURAS AND MARMAS CAN REVIVE HIM INSTANTLY BY AURA CLEANSING.. HE WILL PLACE HIS PALM ON THE NEGATIVE AURA BLACK SPOTS AND THROW IT AWAY LIKE MUD..
THIS WAS ALSO A PERSONAL SKILL OF DANAVA CIVILIZATION PRANIC HEALERS OF INDIA.. A PRANIC HEALER IS NOT A CHANNEL LIKE A REIKI HEALER.
99.9 % OF MODERN DAY PRANIC HEALERS ARE CHARLATANS.. PRANA IS A SANSKRIT/ MALAYALAM WORD..
I HAD WRITTEN ABOUT THE "SWORD OF KRISHNA" GIFTED BY CALICUT KING TO JULIUS CAESAR.. IT COULD ATTACK THE AURA , AS SHIVA LINGAM POWDER WAS USED IN METALLURGY..
https://www.youtube.com/watch?v=CpTB9duiqro&feature=emb_err_watch_on_yt
http://ajitvadakayil.blogspot.com/2017/11/urumi-invincible-kalari-sword-wootz.html
NITYANANDA IS NOT INTO CONVERSIONS..
ASARAM BAPU HAS BEEN JAILED ON THE BASIS OF A UNILATERAL COMPLAINT BY A MENTALLY SICK GIRL.. HIS FOLLOWERS ARE NOW ALL CONVERTED TO CHRISTIANITY.. THESE ARE VULNERABLE PEOPLE WITHOUT LIFE SKILLS , MERELY SURVIVING DAY TO DAY..
VOTES DO NOT DISCRIMINATE..
http://ajitvadakayil.blogspot.com/2013/03/holi-celebrations-immoral-attack-on.html
capt ajit vadakayil
..
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Captain, I have sent the message to CM of Andhra at cm@ap.gov.in
DeleteDear Capt Ajit sir,
DeleteYour 4D thinking has given a breather to Nithyananda devotees, though you were hard on him for goofups...but when it comes to bigger evils like Christian/Muslim proselytizers/Jihadis/terrorists usages...he's doing one good to bring all Hindus together freely...which even Sri Sri or Jaggi Vasudev can't do....Nithyananda's charm cannot be matched...especially so many overseas abhyasis are pouring in money after attending free webinars to visit him and pay for other courses !!!
When R Media could not stomach Art 370 abrogation and Ayodhya cerdict first...then Sabarimala verdict was twisted by Pinari Vijayan to soothe it down without any incident...now TRP's a big slump...so they shifted to Nithyananda...that's all...then Maha surgical strike gave a breather to Nithyananda...;-)
Now, with your scalar wave post above, everyone will read and tone this issue, forever.
Your Registration Number is : PMOPG/E/2019/0674135
DeleteSent emails, tweeted to --
Delete@sardesairajdeep @anjanaomkashyap @awasthis @sagarikaghose @Nidhi @PadmajaJoshi @gauravcsawant @SureshChavhanke @navikakumar @rahulkanwal @Zakka_Jacob @vikramchandra @vineetjaintimes @aroonpurie @nramind @Raghav_Bahl @BDUTT @ShekharGupta @svaradarajan @ravishndtv @nramind @KaranThapar_TTP @PoojaPrasanna4 @DeeptiSachdeva_ @maryashakil @RShivshankar @SwetaSinghAT @ArnabGoswamiRtv @fayedsouza @soniandtv @PrannoyRoyNDTV @SreenivasanJain @Arundatiroy @sunetrac @Sonal_MK @AnchorAnandN @awasthis @shaziailmi @ashutosh83B @_sabanaqvi
Hi Sir,
ReplyDeleteWith all due respect, Nityananda is not a kalari expert or man with huge aura. He is at best a conman who is using Santana Dharma for his personal glory. He may not be into conversions but you only exposed his levitation and agama stories. Why are you backing him now?
Thanks,
Srini
https://www.youtube.com/watch?v=2k0DXRREC2w
DeleteTHE CONMAN IN THE VIDEO ABOVE IS ANDHRA CM JAGAN MOHAN REDDYs SISTERs HUSBAND..
WHY IS HE NOT IN JAIL..
HE CONVERTS HINDUS TO CHRISTIANITY..
I DARE ANIL KUMAR TO TRY HIS MIRACLES ON ME..
WHO IS PADMA VIBHUSHAN SADGURU JAGGI VASUDEV... HIS WIFEs FATHER ACCUSED HIM OF MURDERING HIS DAUGHTER.. HE CONVERTED SHIVA TO A MORTAL..
WHO IS PADMA VIBHUSAN SRI SRI RAVISHANKAR? THE LEAST I SAY ABOUT HIM THE BETTER.. SADGURU IS ATLEAST MENTALLY SHARP..
capt ajit vadakayil
..
DeleteAllow meto say what will happen to him, he will fall phutt on his face if he dares to perform these fake acts on you, and will bleed mentally if not physically and that's really painful..
Just my two cents guruji
Don't go against guruji, he controls the conscience..
Sir, Yesterday I watched the movie Frozen 2 from Disney with my daughter. The entire movie is based on water having memory. I first came to know about water holding from your blog and now everyone is talking about it.
ReplyDeleteTHE SECRET OF THEERTHAM IS THAT IT IS H 1.5 O.. ( NOT H2 O )… WATER MOLECULE CHANGES PARTNERS A HUNDRED BILLION TIMES A SECOND.
DeleteIT IS SCALAR ENERGY SCIENCE .. THIS IS QUANTUM SCIENCE NOT CLASSICAL SCIENCE.. THEERTHAM WATER STRUCTURE IS AFFECTED BY THE EMOTIONS OF PEOPLE.
VEDAS ARE INFALLIBLE..
MOBIUS COIL FLOW IN GANGES WATER ( KASHI GHATS ) EMIT A GIANT SCALAR FIELD.. THE FIELD OF BRAHMAN IS SCALAR..
WATER IS A TINY BENT MOLECULE WITH THE MOLECULAR FORMULA H2O, CONSISTING OF TWO LIGHT HYDROGEN ATOMS ATTACHED TO EACH 16-FOLD HEAVIER OXYGEN ATOM. EACH MOLECULE IS ELECTRICALLY NEUTRAL BUT POLAR, WITH THE CENTER OF POSITIVE AND NEGATIVE CHARGES LOCATED IN DIFFERENT PLACES..
THE REASON WATER HAS A BENT SHAPE IS THAT THE TWO LONE PAIR OF ELECTRONS ARE ON THE SAME SIDE OF THE MOLECULE.
THE TWO HYDROGEN ATOMS AND THE TWO LONE ELECTRON PAIRS ARE AS FAR APART AS POSSIBLE AT NEARLY 108 DEGREES BOND ANGLE. 108 IS THE DIGITAL VALUE OF HINDU KING MANTRA OM..
THE WATER MOLECULE IS BENT MOLECULAR GEOMETRY BECAUSE THE LONE ELECTRON PAIRS, ALTHOUGH STILL EXERTING INFLUENCE ON THE SHAPE, ARE INVISIBLE WHEN LOOKING AT MOLECULAR GEOMETRY.
QUANTUM COMPUTERS WILL TAKE OFF ONLY WHEN SILICON IS REPLACED WITH LIVING GANGES WATER , AND WIRING IS ORGANIC LIKE DNA..
WATER IS A POLAR MOLECULE AND ALSO ACTS AS A POLAR SOLVENT. WHEN A CHEMICAL SPECIES IS SAID TO BE "POLAR," THIS MEANS THAT THE POSITIVE AND NEGATIVE ELECTRICAL CHARGES ARE UNEVENLY DISTRIBUTED.
THE POSITIVE CHARGE COMES FROM THE ATOMIC NUCLEUS, WHILE THE ELECTRONS SUPPLY THE NEGATIVE CHARGE. IT'S THE MOVEMENT OF ELECTRONS THAT DETERMINES POLARITY.
WATER (H2O) IS POLAR BECAUSE OF THE BENT SHAPE OF THE MOLECULE. THE SHAPE MEANS MOST OF THE NEGATIVE CHARGE FROM THE OXYGEN ON SIDE OF THE MOLECULE AND THE POSITIVE CHARGE OF THE HYDROGEN ATOMS IS ON THE OTHER SIDE OF THE MOLECULE. THIS IS AN EXAMPLE OF POLAR COVALENT CHEMICAL BONDING.
THE ELECTRONEGATIVITY VALUE OF HYDROGEN IS 2.1, WHILE THE ELECTRONEGATIVITY OF OXYGEN IS 3.5. THE SMALLER THE DIFFERENCE BETWEEN ELECTRONEGATIVITY VALUES, THE MORE LIKELY ATOMS WILL FORM A COVALENT BOND. A LARGE DIFFERENCE BETWEEN ELECTRONEGATIVITY VALUES IS SEEN WITH IONIC BONDS
BOTH HYDROGEN ATOMS ARE ATTRACTED TO THE SAME SIDE OF THE OXYGEN ATOM, BUT THEY ARE AS FAR APART FROM EACH OTHER AS THEY CAN BE BECAUSE THE HYDROGEN ATOMS BOTH CARRY A POSITIVE CHARGE. THE BENT CONFORMATION IS A BALANCE BETWEEN ATTRACTION AND REPULSION.
REMEMBER THAT EVEN THOUGH THE COVALENT BOND BETWEEN EACH HYDROGEN AND OXYGEN IN WATER IS POLAR, A WATER MOLECULE IS AN ELECTRICALLY NEUTRAL MOLECULE OVERALL. EACH WATER MOLECULE HAS 10 PROTONS AND 10 ELECTRONS, FOR A NET CHARGE OF 0.
WATER ACTS AS A POLAR SOLVENT BECAUSE IT CAN BE ATTRACTED TO EITHER THE POSITIVE OR NEGATIVE ELECTRICAL CHARGE ON A SOLUTE. THE SLIGHT NEGATIVE CHARGE NEAR THE OXYGEN ATOM ATTRACTS NEARBY HYDROGEN ATOMS FROM WATER OR POSITIVE-CHARGED REGIONS OF OTHER MOLECULES.
I HAVE CHEMICAL TANK CLEANING SECRETS WHICH WILL BE REVEALED ONLY WHEN MY REVELATIONS REACH 98%.
http://ajitvadakayil.blogspot.com/2010/11/water-valley-and-walking-on-water-capt.html
HOMEOPATHY ALL OVER THE WORLD HAS BEEN HIJACKED BY JEW ROTHSCHILD.. IN INDIA THEY USE ALCOHOL .. SORRY, THE PROPERTY OF “ WATER HOLDING MEMORY” IS THE BASE OF HOMEOPATHY.
WATER CAN RETAIN A "MEMORY" OF SOLUTE PARTICLES AFTER ARBITRARILY LARGE DILUTION. .. EVEN WHEN THEY ARE DILUTED TO THE POINT THAT NO MOLECULE OF THE ORIGINAL SUBSTANCE REMAINS.
CAPT AJIT VADAKAYIL DEMANDS OF INDIAS HEALTH MINISTER.. USE WATER IN HOMEOPATHY—NEVER ALCOHOL..
http://ajitvadakayil.blogspot.com/2011/01/living-water-capt-ajit-vadakayil.html
Capt ajit vadakayil
..
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Teertham water will be the best memory chip!!
DeleteCaptain, have sent the message to health minister at hfwminister@gov.in. The email went through and I have asked for an acknowledgement.
DeleteNamaste
Delete"QUANTUM COMPUTERS WILL TAKE OFF ONLY WHEN SILICON IS REPLACED WITH LIVING GANGES WATER , AND WIRING IS ORGANIC LIKE DNA.."
Reading stuff like this on your blogsite makes me feel like I am high on cannabis. Not some normal state thinking, sir. Mind blowing!
Sent emails to many..
Deletehttps://www.facebook.com/MoHFWIndia/
appointments-hfm@gov.in,
abhinav.gupta@gov.in,
Sadanand.ranjan@gmail.com,
agam.mittal@gov.in,
bmm.das@gov.in,
arun.dgsnd@nic.in,
hfwminister@gov.in,
kuldip.narayan@gov.in,
ashanareshdelhi@yahoo.com,
secyhfw@nic.in -- Asked for ack.
Readers can found email ids here --
https://mohfw.gov.in/department-health-and-family-welfare-directory
https://mohfw.gov.in/sites/default/files/MoHFW%20Directory_7.pdf
Your Grievance is registered successfully.
DeleteRegistration Number : DHLTH/E/2019/05690
Wonderful knowledge sir.. Thank you.
Your Registration Number is : PMOPG/E/2019/0674130
Deletehttps://www.youtube.com/watch?v=0omja1ivpx0
ReplyDeleteNOBODY HAS ROCKED THE WAY FREDDY MERCURY ROCKED .
HE WAS AN ANAL SEX RECEIVING PARSI.. A FREAK WITH A 4 OCTAVE VOCAL RANGE..
https://www.youtube.com/watch?v=p3MjsrMNCbU
TODAY WE HAVE CHOOTS LIKE FARHAN AKHTAR THINKING HE CAN ROCK.. ZINDAGI NA MILEGI DOOBAARAAAAAAAAAA.. FUCKIN" CUNT.
capt ajit vadakayil
..
Captain, how did Aurangzeb executed guru tez bahadur ?
ReplyDeleteVia TrueIndology
ReplyDeleteSo far as the so called "Fibonacci Series" is concerned, Fibonacci was only TRANSLATING the Sutras of Pingala (c.3rd century CE) and his commentator Virahanka who derived "Fibonacci Series" several hundreds of years before Fibonacci was even born
Fibonacci's introduction makes it clear that he considered himself "Indian Mathematician" insomuch as he adhered to Indian Mathematical Methodology and contributed to it.
The real name of the so called "Fibonacci Series" is "Indian Series".
This comes from the horse's mouth !
The precepts of Pythagoras and Euclid were forgotten in early middle ages and revived only later.
Yet, the credit always goes to Pythagoras and Euclid. Never to the later day Mathematicians who revived their works. Why is Pingala never extended the same courtesy?
twitter.com/tiinexile
The current political events in Maharashtra have brought out dirt of all parties and all politicians.
ReplyDeleteNot a single politician stands clean.
No moral person can support anyone of them.
All are fully exposed.
Public has taken notice.
https://www.instagram.com/p/B4z99x1l2WM/?igshid=13h98oocv5ruc
DeleteThe Sena leader Sanjay ROT has openly admitted that "Nobody is a saint in politics". The wealth trapped in Mumbai and its downtown underworld is too captivating. It is ricer than Bangalore. The attention has shifted from Karnataka to Maharashtra now.
DeleteThe heroic Queen Artemisia I of Caria, she was also a Kerala Thiyya Queen right?
ReplyDeleteShe ruled the same coastal regions of Turkey in Mediterranean sea that many Thiyyars did, and they were satraps of the Achaemenid Empire as well.
ARTEMISIA I OF CARIA BORN IN THE ISLAND OF CRETE WAS A KERALA THIYYA QUEEN— LIKE QUEEN DIDO.
Deletehttp://ajitvadakayil.blogspot.com/2019/05/the-ancient-7000-year-old-shakti.html
capt ajit vadakayil
..
Dear Capt Ajit sir,
ReplyDeleteNow with this Theertham explanation, nobody can challenge you on chemistry, you are much more than nobel prize any day....but this one post alone should get a divine mention/grace...biggest revelation for me...it kindled my spirit...will remember your post when taking divine theertham always...thanks....we are ever grateful to you.
On topic of water Victor Schauberger an Austrian forestry official has made discoveries about temperature and capacity to carry.
ReplyDeletehttps://youtu.be/bdynEiXFypA
Dear Sir,
ReplyDeleteDo Quantum computers have souls?
NO
DeleteDear Sir ,
ReplyDeletewhat are your thoughts on the story of Jaya and Vijaya and its relation to danava civilization
Hello Sir,
ReplyDeleteIs this true ?
One of walls of Kundadam Vadakkunath Swami Temple is in Thrissur, Kerala
How can anyone sculp baby in Mother's Womb by looking at the abdomen ( 2000 years before X Ray's were Invented ) the temple has various poses of baby's of different months
COMMENTS IN THIS POST IS NOW CLOSED..
ReplyDeletehttps://www.livemint.com/news/world/crop-insurance-flaws-fuel-farm-distress-11574185759756.html
ReplyDelete