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Python Deep Learning

By : Vasilev, Daniel Slater, Spacagna, Roelants, Zocca

4 (8)

Python Deep Learning

4 (8)

By: Vasilev, Daniel Slater, Spacagna, Roelants, Zocca

Overview of this book

With the surge in artificial intelligence in applications catering to both business and consumer needs, deep learning is more important than ever for meeting current and future market demands. With this book, you’ll explore deep learning, and learn how to put machine learning to use in your projects. This second edition of Python Deep Learning will get you up to speed with deep learning, deep neural networks, and how to train them with high-performance algorithms and popular Python frameworks. You’ll uncover different neural network architectures, such as convolutional networks, recurrent neural networks, long short-term memory (LSTM) networks, and capsule networks. You’ll also learn how to solve problems in the fields of computer vision, natural language processing (NLP), and speech recognition. You'll study generative model approaches such as variational autoencoders and Generative Adversarial Networks (GANs) to generate images. As you delve into newly evolved areas of reinforcement learning, you’ll gain an understanding of state-of-the-art algorithms that are the main components behind popular games Go, Atari, and Dota. By the end of the book, you will be well-versed with the theory of deep learning along with its real-world applications.

Preface

Preface

Who this book is for

What this book covers

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Machine Learning - an Introduction

Machine Learning - an Introduction

Introduction to machine learning

Different machine learning approaches

Neural networks

Summary

Neural Networks

Neural Networks

The need for neural networks

An introduction to neural networks

Training neural networks

Summary

Deep Learning Fundamentals

Deep Learning Fundamentals

Introduction to deep learning

Fundamental deep learning concepts

Deep learning algorithms

Applications of deep learning

The reasons for deep learning's popularity

Introducing popular open source libraries

Summary

Computer Vision with Convolutional Networks

Computer Vision with Convolutional Networks

Intuition and justification for CNN

Convolutional layers

Stride and padding in convolutional layers

Pooling layers

The structure of a convolutional network

Improving the performance of CNNs

A CNN example with Keras and CIFAR-10

Summary

Advanced Computer Vision

Advanced Computer Vision

Transfer learning

Advanced network architectures

Capsule networks

Advanced computer vision tasks

Artistic style transfer

Summary

Generating Images with GANs and VAEs

Generating Images with GANs and VAEs

Intuition and justification of generative models

Variational autoencoders

Generative Adversarial networks

Summary

Recurrent Neural Networks and Language Models

Recurrent Neural Networks and Language Models

Recurrent neural networks

Language modeling

Sequence to sequence learning

Speech recognition

Summary

Reinforcement Learning Theory

Reinforcement Learning Theory

RL paradigms

RL as a Markov decision process

Finding optimal policies with Dynamic Programming

Monte Carlo methods

Temporal difference methods

Value function approximations

Experience replay

Q-learning in action

Summary

Deep Reinforcement Learning for Games

Deep Reinforcement Learning for Games

Introduction to genetic algorithms playing games

Deep Q-learning

Policy gradient methods

Model-based methods

Summary

Deep Learning in Autonomous Vehicles

Deep Learning in Autonomous Vehicles

Brief history of AV research

AV introduction

Imitiation driving policy

Driving policy with ChauffeurNet

DL in the Cloud

Summary

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Recurrent neural networks

RNN is a type of neural network, which can process sequential data with variable length. Examples of such data include the words of a sentence or the price of a stock in various moments of time. By using the word sequential, we imply that the elements of the sequence are related to each other and their order matters. For example, if we take a book and shuffle randomly all the words in it, the text will loose it's meaning, even though we'll still know the individual words.

RNNs get their name because they apply the same function over a sequence recurrently. We can define an RNN as a recurrence relation:

Here, f is a differentiable function, s_t is a vector of values called internal network state (at step t), and x_t is the network input at step t. Unlike regular networks, where the state only depends on the current input (and network weights)...

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