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Python Deep Learning

By : Valentino Zocca, Gianmario Spacagna, Daniel Slater, Peter Roelants

4.1 (10)

Python Deep Learning

4.1 (10)

By: Valentino Zocca, Gianmario Spacagna, Daniel Slater, Peter 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

Hyper-parameters tuning

Following the design of our deep neural network according to the previous sections, we would end up with a bunch of parameters to tune. Some of them have default or recommended values and do not require expensive fine-tuning. Others strongly depends on the underlying data, specific application domain, and a set of other components. Thus, the only way to find best values is to perform a model selection by validating based on the desired metric computed on the validation data fold.

Now we will list a table of parameters that we might want to consider tuning. Please consider that each library or framework may have additional parameters and a custom way of setting them. This table is derived from the available tuning options in H2O. It summarizes the common parameters, but not all of them, when building a deep auto-encoder network in production:

Parameter	Description	Recommended value(s)
`activation`	The differentiable activation function.	Depends on the data nature...

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Tech Concepts

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Programming languages

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