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Python Deep Learning

By : Zocca, Spacagna, Daniel Slater, Roelants

4.1 (10)

Python Deep Learning

4.1 (10)

By: Zocca, Spacagna, Daniel Slater, Roelants

Overview of this book

With an increasing interest in AI around the world, deep learning has attracted a great deal of public attention. Every day, deep learning algorithms are used broadly across different industries. The book will give you all the practical information available on the subject, including the best practices, using real-world use cases. You will learn to recognize and extract information to increase predictive accuracy and optimize results. Starting with a quick recap of important machine learning concepts, the book will delve straight into deep learning principles using Sci-kit learn. Moving ahead, you will learn to use the latest open source libraries such as Theano, Keras, Google's TensorFlow, and H20. Use this guide to uncover the difficulties of pattern recognition, scaling data with greater accuracy and discussing deep learning algorithms and techniques. Whether you want to dive deeper into Deep Learning, or want to investigate how to get more out of this powerful technology, you’ll find everything inside.

Preface

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Errata

Free Chapter

1. Machine Learning – An Introduction

1. Machine Learning – An Introduction

What is machine learning?

Different machine learning approaches

Summary

2. Neural Networks

2. Neural Networks

Why neural networks?

Fundamentals

Summary

3. Deep Learning Fundamentals

3. Deep Learning Fundamentals

What is deep learning?

Deep learning applications

GPU versus CPU

Popular open source libraries – an introduction

Summary

4. Unsupervised Feature Learning

4. Unsupervised Feature Learning

Autoencoders

Restricted Boltzmann machines

Summary

5. Image Recognition

5. Image Recognition

Similarities between artificial and biological models

Intuition and justification

Convolutional layers

Pooling layers

Dropout

Convolutional layers in deep learning

Convolutional layers in Theano

A convolutional layer example with Keras to recognize digits

A convolutional layer example with Keras for cifar10

Pre-training

Summary

6. Recurrent Neural Networks and Language Models

6. Recurrent Neural Networks and Language Models

Recurrent neural networks

Language modeling

Speech recognition

Summary

Bibliography

7. Deep Learning for Board Games

7. Deep Learning for Board Games

Early game playing AI

Using the min-max algorithm to value game states

Implementing a Python Tic-Tac-Toe game

Learning a value function

Training AI to master Go

Upper confidence bounds applied to trees

Deep learning in Monte Carlo Tree Search

Quick recap on reinforcement learning

Policy gradients for learning policy functions

Policy gradients in AlphaGo

Summary

8. Deep Learning for Computer Games

8. Deep Learning for Computer Games

A supervised learning approach to games

Applying genetic algorithms to playing games

Q-Learning

Q-learning in action

Dynamic games

Atari Breakout

Actor-critic methods

Asynchronous methods

Model-based approaches

Summary

9. Anomaly Detection

9. Anomaly Detection

What is anomaly and outlier detection?

Real-world applications of anomaly detection

Popular shallow machine learning techniques

Anomaly detection using deep auto-encoders

H2O

Examples

Summary

10. Building a Production-Ready Intrusion Detection System

10. Building a Production-Ready Intrusion Detection System

What is a data product?

Training

Testing

Model validation

Hyper-parameters tuning

End-to-end evaluation

Deployment

Summary

Index

Index

Customer Reviews

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Conventions

In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning.

Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: The code above for drawing should be immediately clear, we just notice that the line importing cm.

A block of code is set as follows:

(X_train, Y_train), (X_test, Y_test) = cifar10.load_data()
X_train = X_train.reshape(50000, 3072)
X_test = X_test.reshape(10000, 3072)
input_size = 3072

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

def monte_carlo_tree_search_uct(board_state, side, number_of_rollouts):
    state_results = collections.defaultdict(float)
    state_samples = collections.defaultdict(float)

Any command-line input or output is written as follows:

git clone https://github.com/fchollet/keras.git
cd keras
python setup.py install

New terms and important words are shown in bold.

Note

Warnings or important notes appear in a box like this.

Tip

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