Deep Learning, Vol. 2 From Basics to Practice

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Book Description

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This book is designed to give you that understanding. You'll be able to choose the right kind of architecture, how to build a system that can learn, how to train it, and then how to use it to accomplish your goals. You'll be able to read and understand the documentation for whatever library you'd like to use. And you'll be able to follow exciting, on-going breakthroughs as they appear, because you'll have the knowledge and vocabulary that let you read new material, and discuss it with other people doing deep learning.

The book is extensively illustrated with over 1000 original figures. They are also all available for free download, for your own use.

You don't need any previous experience with machine learning or deep learning for this book. You don't need to be a mathematician, because there's nothing in the book harder than the occasional multiplication. You don't need to choose a particular programming language, or library, or piece of hardware, because our approach is largely independent of those things. Our focus is on the principles and techniques that are applicable to any language, library, and hardware.

Even so, practical programming is important. To stay focused, we gather our programming discussions into 3 chapters that show how to use two important and free Python libraries. Both chapters come with extensive Jupyter notebooks that contain all the code. Other chapters also offer notebooks for for every Python-generated figure

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Table of Contents

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Volume 2 --

Deep Learning

Convolutional Neural Nets (CNNs)

Recurrent Nerual Nets (RNNs)

Keras Part 1

Keras Part 2

Autoencoders

Reinforcement Learning

Generative Adversarial Networks (GANs)

Creative Applications

Datasets

Glossary