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Python Deep Learning

Python Deep Learning

By : Zocca, Spacagna, Daniel Slater, Roelants
4.1 (10)
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Python Deep Learning

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.
Table of Contents (12 chapters)
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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
1. Machine Learning – An Introduction
1. Machine Learning – An Introduction
What is machine learning?
Different machine learning approaches
Summary
2
2. Neural Networks
2. Neural Networks
Why neural networks?
Fundamentals
Summary
3
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
4. Unsupervised Feature Learning
4. Unsupervised Feature Learning
Autoencoders
Restricted Boltzmann machines
Summary
5
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
6. Recurrent Neural Networks and Language Models
6. Recurrent Neural Networks and Language Models
Recurrent neural networks
Language modeling
Speech recognition
Summary
Bibliography
7
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
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
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
10. Building a Production-Ready Intrusion Detection System
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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
11
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...

End of Section 6
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