Python Deep Learning

Take your machine learning skills to the next level by mastering Deep Learning concepts and algorithms using Python.

Python Deep Learning

Valentino Zocca et al.

2 customer reviews
Take your machine learning skills to the next level by mastering Deep Learning concepts and algorithms using Python.
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Book Details

ISBN 139781786464453
Paperback406 pages

Book Description

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

Chapter 1: Machine Learning – An Introduction
What is machine learning?
Different machine learning approaches
Summary
Chapter 2: Neural Networks
Why neural networks?
Fundamentals
Summary
Chapter 3: Deep Learning Fundamentals
What is deep learning?
Deep learning applications
GPU versus CPU
Popular open source libraries – an introduction
Summary
Chapter 4: Unsupervised Feature Learning
Autoencoders
Restricted Boltzmann machines
Summary
Chapter 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
Chapter 6: Recurrent Neural Networks and Language Models
Recurrent neural networks
Language modeling
Speech recognition
Summary
Bibliography
Chapter 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
Chapter 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
Chapter 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
Chapter 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

What You Will Learn

  • Get a practical deep dive into deep learning algorithms
  • Explore deep learning further with Theano, Caffe, Keras, and TensorFlow
  • Learn about two of the most powerful techniques at the core of many practical deep learning implementations: Auto-Encoders and Restricted Boltzmann Machines
  • Dive into Deep Belief Nets and Deep Neural Networks
  • Discover more deep learning algorithms with Dropout and Convolutional Neural Networks
  • Get to know device strategies so you can use deep learning algorithms and libraries in the real world

Authors

Table of Contents

Chapter 1: Machine Learning – An Introduction
What is machine learning?
Different machine learning approaches
Summary
Chapter 2: Neural Networks
Why neural networks?
Fundamentals
Summary
Chapter 3: Deep Learning Fundamentals
What is deep learning?
Deep learning applications
GPU versus CPU
Popular open source libraries – an introduction
Summary
Chapter 4: Unsupervised Feature Learning
Autoencoders
Restricted Boltzmann machines
Summary
Chapter 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
Chapter 6: Recurrent Neural Networks and Language Models
Recurrent neural networks
Language modeling
Speech recognition
Summary
Bibliography
Chapter 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
Chapter 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
Chapter 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
Chapter 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

Book Details

ISBN 139781786464453
Paperback406 pages
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From 2 reviews

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