KUMAR - Learning in Python: Learn Data Science and Machine Learning with Modern Neural Networks composed in Python, Theano, and TensorFlow

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Learning in Python

Learn Data Science and Machine Learning with Modern Neural Networks composed in Python, Theano, and TensorFlow

MADHAN KUMAR

Table of Contents

Introduction

Chapter 1 : What is a neural network?

Chapter 2 : Biological analogies

Chapter 3 : Getting output from a neural network

Chapter 4 : Training a neural network with backpropagation

Chapter 5 : Theano

Chapter 6 : TensorFlow

Chapter 7 : Improving backpropagation with modern techniques - momentum, adaptive learning rate, and regularization

Chapter 8 : Unsupervised learning, autoencoders, restricted Boltzmann machines, convolutional neural networks, and LSTMs

Chapter 9 : You know more than you think you know

Conclusion

Introduction

Profound learning is causing a ripple effect. At the hour of this composition (March 2016), Google's AlghaGo program just beat 9-dan proficient Go player Lee Sedol at the round of Go, a Chinese table game.

Specialists in the field of Artificial Intelligence thought we were 10 years from accomplishing a triumph against a top proficient Go player, yet progress appears to have quickened!

While profound learning is an intricate subject, it isn't any harder to learn than some other AI calculation. I composed this book to acquaint you with the essentials of neural organizations. You will coexist fine with undergrad level math and programming aptitude.

All the materials in this book can be downloaded and introduced for nothing. We will utilize the Python programming language, alongside the mathematical processing library Numpy. I will likewise show you in the later sections how to manufacture a profound organization utilizing Theano and TensorFlow, which are libraries constructed explicitly for profound learning and can quicken calculation by exploiting the GPU.

Not at all like other AI calculations, is profound learning especially incredible because it naturally learns highlights. That implies you don't have to invest your energy attempting to think of and test "bits" or "connection impacts" - something just analysts love to do. All things considered, we will let the neural organization get familiar with these things for us. Each layer of the neural organization learns an unexpected reflection in comparison to the past layers. For instance, in picture characterization, the principal layer may learn various strokes, and in the following layer set up the strokes to learn shapes, and in the following layer set up the shapes to frame facial highlights, and in the following layer have a significant level portrayal of countenances.

Do you need a delicate prologue to this "dark art", with down to earth code models that you can attempt immediately and apply to your own information? At that point, this book is for you.

Chapter 1

What is a neural network?

A neural organization is called such because sooner or later ever, PC researchers were attempting to display the mind in PC code.

The inevitable objective is to make "counterfeit general knowledge", which to me implies a program that can pick up anything you or I can learn. We are not there yet, so no compelling reason to get terrified about the machines assuming control over mankind. As of now, neural organizations are truly adept at performing particular errands, such as ordering pictures and discourse.

In contrast to the mind, these counterfeit neural organizations have an extremely exacting predefined structure.

The mind is comprised of neurons that discuss one another through electrical and compound signs (henceforth the term, neural organization). We don't separate between these 2 kinds of signs in fake neural organizations, so starting now and into the foreseeable future, we will simply say "a" signal is being passed starting with one neuron then onto the next.

Signs are passed starting with one neuron then onto the next using what is called an "action potential". It is a spike in power along with the cell layer of a neuron. The intriguing thing about activity possibilities is that possibly they occur, or they don't. There is no "in the middle". This is known as the "win or bust" guideline. The following is a plot of the activity possible versus time, with genuine, actual units.

These associations between neurons have qualities. You may have heard the expression, "neurons that fire together, wire together", which is ascribed to the Canadian neuropsychologist Donald Hebb.

Neurons with solid associations will be turned "on" by one another. So on the off chance that one neuron imparts a sign (activity potential) to another neuron, and their association is solid, at that point the following neuron will likewise have an activity potential, would could then be given to different neurons, and so forth

If the association between 2 neurons is feeble, at that point one neuron imparting a sign to another neuron may cause a little expansion in electrical potential at the second neuron, however insufficient to cause another activity potential.

Accordingly, we can think about a neuron being "on" or "off". (for example it has an activity potential, or it doesn't)

What does this remind you of?

If you said digital computers, then you would be right!

In particular, neurons are the ideal model for a yes-no, genuine bogus, 0/1 kind of issue. We call this "twofold grouping" and the AI similarity would be the "calculated relapse" calculation.

The above picture is a pictorial portrayal of the calculated relapse model. It takes as data sources x1, x2, and x3, which you can envision as the yields of different neurons or some other info signal (for example the visual receptors in your eyes or the mechanical receptors in your fingertips), and yields another sign which is a blend of these sources of info, weighted by the quality of those information neurons to this yield neuron.

Since we must in the long run manage genuine numbers and equations, how about we take a gander at how we can ascertain y from x.

y = sigmoid(w1*x1 + w2*x2 + w3*x3)

Note that in this book, we will overlook the inclination term, since it can undoubtedly be remembered for the given equation by adding measurement x0 which is consistently equal to 1.

So each info neuron gets duplicated by its comparing weight (synaptic quality) and added to all the others. We at that point apply for a "sigmoid" work on top of that to get the yield y. The sigmoid is characterized as:

sigmoid(x) = 1 / (1 + exp(-x))

If you somehow managed to plot the sigmoid, you would get this:

You can see that the yield of a sigmoid is consistently somewhere in the range of 0 and 1. It has 2 asymptotes, so the yield is actually 1 when the information is + limitlessness, and the yield is actually 0 when the info is - boundlessness.

The yield is 0.5 when the information is 0.

You can decipher the yield as a likelihood. Specifically, we decipher it as the likelihood:

P(Y=1 | X)

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