Book Image

Advanced Machine Learning with Python

Book Image

Advanced Machine Learning with Python

Overview of this book

Designed to take you on a guided tour of the most relevant and powerful machine learning techniques in use today by top data scientists, this book is just what you need to push your Python algorithms to maximum potential. Clear examples and detailed code samples demonstrate deep learning techniques, semi-supervised learning, and more - all whilst working with real-world applications that include image, music, text, and financial data. The machine learning techniques covered in this book are at the forefront of commercial practice. They are applicable now for the first time in contexts such as image recognition, NLP and web search, computational creativity, and commercial/financial data modeling. Deep Learning algorithms and ensembles of models are in use by data scientists at top tech and digital companies, but the skills needed to apply them successfully, while in high demand, are still scarce. This book is designed to take the reader on a guided tour of the most relevant and powerful machine learning techniques. Clear descriptions of how techniques work and detailed code examples demonstrate deep learning techniques, semi-supervised learning and more, in real world applications. We will also learn about NumPy and Theano. By this end of this book, you will learn a set of advanced Machine Learning techniques and acquire a broad set of powerful skills in the area of feature selection & feature engineering.

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Advanced Machine Learning with Python
Jul, 2016 | 278 pages
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Table of Contents (17 chapters)
Advanced Machine Learning with Python
Advanced Machine Learning with Python
Credits
Credits
About the Author
About the Author
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
Unsupervised Machine Learning
Unsupervised Machine Learning
Principal component analysis
Introducing k-means clustering
Self-organizing maps
Further reading
Summary
2
Deep Belief Networks
Deep Belief Networks
Neural networks – a primer
Restricted Boltzmann Machine
Deep belief networks
Further reading
Summary
3
Stacked Denoising Autoencoders
Stacked Denoising Autoencoders
Autoencoders
Stacked Denoising Autoencoders
Further reading
Summary
4
Convolutional Neural Networks
Convolutional Neural Networks
Introducing the CNN
Further Reading
Summary
5
Semi-Supervised Learning
Semi-Supervised Learning
Introduction
Understanding semi-supervised learning
Semi-supervised algorithms in action
Further reading
Summary
6
Text Feature Engineering
Text Feature Engineering
Introduction
Text feature engineering
Further reading
Summary
7
Feature Engineering Part II
Feature Engineering Part II
Introduction
Creating a feature set
Feature engineering in practice
Further reading
Summary
8
Ensemble Methods
Ensemble Methods
Introducing ensembles
Using models in dynamic applications
Further reading
Summary
9
Additional Python Machine Learning Tools
Additional Python Machine Learning Tools
Alternative development tools
Further reading
Summary
Chapter Code Requirements
Chapter Code Requirements
Index
Index

Neural networks – a primer

The RBM is a form of recurrent neural network. In order to understand how the RBM works, it is necessary to have a more general understanding of neural networks. Readers with an understanding of artificial neural network (hereafter neural network, for the sake of simplicity) algorithms will find familiar elements in the following description.

There are many accounts that cover neural networks in great theoretical detail; we won't go into great detail retreading this ground. For the purposes of this chapter, we will first describe the components of a neural network, common architectures, and prevalent learning processes.

The composition of a neural network

For unfamiliar readers, neural networks are a class of mathematical models that train to produce and optimize a definition for a function (or distribution) over a set of input features. The specific objective of a given neural network application can be defined by the operator using a performance measure (typically...

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