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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

A convolutional layer example with Keras to recognize digits

In the third chapter, we introduced a simple neural network to classify digits using Keras and we got 94%. In this chapter, we will work to improve that value above 99% using convolutional networks. Actual values may vary slightly due to variability in initialization.

First of all, we can start by improving the neural network we had defined by using 400 hidden neurons and run it for 30 epochs; that should get us already up to around 96.5% accuracy:

    hidden_neurons = 400
    epochs = 30

Next we could try scaling the input. Images are comprised of pixels, and each pixel has an integer value between 0 and 255. We could make that value a float and scale it between 0 and 1 by adding these four lines of code right after we define our input:

X_train = X_train.astype('float32')     
X_test = X_test.astype('float32')     
X_train /= 255     
X_test /= 255

If we run our network now, we get a poorer accuracy, just above 92%, but we need not...

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