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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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11
Index

Summary

We have seen in this chapter two of the most powerful techniques at the core of many practical deep learning implementations: autoencoders and restricted Boltzmann machines.

For both of them, we started with the shallow example of one hidden layer, and we explored how we can stack them together to form a deep neural network able to automatically learn high-level and hierarchical features without requiring explicit human knowledge.

They both serve similar purposes, but there is a little substantial difference.

Autoencoders can be seen as a compression filter that we use to compress the data in order to preserve only the most informative part of it and be able to deterministically reconstruct an approximation of the original data. Autoencoders are an elegant solution to dimensionality reduction and non-linear compression bypassing the limitations of the principal component analysis (PCA) technique. The advantages of autoencoders are that they can be used as preprocessing steps for further...

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