Deep Learning Made Easy with R: A Gentle Introduction For Data Science

Master Deep Learning with this fun, practical, hands on guide.

With the explosion of big data deep learning is now on the radar. Large companies such as Google, Microsoft, and Facebook have taken notice, and are actively growing in-house deep learning teams. Other large corporations are quickly building out their own teams. If you want to join the ranks of today’s top data scientists take advantage of this valuable book. It will help you get started. It reveals how deep learning models work, and takes you under the hood with an easy to follow process showing you how to build them faster than you imagined possible using the powerful, free R predictive analytics package.
Bestselling decision scientist Dr. N.D Lewis shows you the shortcut up the steep steps to the very top. It’s easier than you think. Through a simple to follow process you will learn how to build the most successful deep learning models used for learning from data. Once you have mastered the process, it will be easy for you to translate your knowledge into your own powerful applications.
If you want to accelerate your progress, discover the best in deep learning and act on what you have learned, this book is the place to get started.
YOU’LL LEARN HOW TO:

• Understand Deep Neural Networks
• Use Autoencoders
• Unleash the power of Stacked Autoencoders
• Leverage the Restricted Boltzmann Machine
• Develop Recurrent Neural Networks
• Master Deep Belief Networks

Everything you need to get started is contained within this book. It is your detailed, practical, tactical hands on guide – the ultimate cheat sheet for deep learning mastery. A book for everyone interested in machine learning, predictive analytic techniques, neural networks and decision science. Start building smarter models today using R!
Buy the book today. Your next big breakthrough using deep learning is only a page away!

Table of Contents

Chapter 1 Introduction
Chapter 2 Deep Neural Networks
Chapter 3 Elman Neural Networks
Chapter 4 Jordan Neural Networks
Chapter 5 The Secret to the Autoencoder
Chapter 6 The Stacked Autoencoder in a Nutshell
Chapter 7 Restricted Boltzmann Machines
Chapter 8 Deep Belief Networks