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Python Deep Learning - Second Edition

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

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Python Deep Learning

4 (8)

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

Buy this Book

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

Who this book is for

What this book covers

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Free Chapter

Machine Learning - an Introduction

Introduction to machine learning

Different machine learning approaches

Neural networks

Summary

Neural Networks

The need for neural networks

An introduction to neural networks

Training neural networks

Summary

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

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

Transfer learning

Advanced network architectures

Capsule networks

Advanced computer vision tasks

Artistic style transfer

Summary

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

Language modeling

Sequence to sequence learning

Speech recognition

Summary

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

Introduction to genetic algorithms playing games

Deep Q-learning

Policy gradient methods

Model-based methods

Summary

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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Policy gradient methods

All RL algorithms we discussed until now have tried to learn the state- or action-value functions. For example, in Q-learning we usually follow an ε-greedy policy, which has no parameters (OK, it has one parameter) and relies on the value function instead. In this section, we'll discuss something new: how to approximate the policy itself with the help of policy gradient methods. We'll follow a similar approach as in Chapter 8, Reinforcement Learning Theory, in the Value function approximation section.

There, we introduced a value approximation function, which is described by a set of parameters w (neural net weights). Here, we'll introduce a parameterized policy , which is described by a set of parameters θ. As with value function approximation, θ could be the weights of a neural network.

Recall that we use the notation...