Deep learning made easy with R

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 analytic package. No experience required. Bestselling data 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. For the data scientist who wants to use deep learning. 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: Create Deep Neural Networks; Develop Recurrent Neural Networks; Build Elman Neural Networks; Deploy Jordan Neural Networks; Understand the Autoencoder; Use Sparse Autoencoders; Unleash the power of Stacked Autoencoders; Leverage the Restricted Boltzmann Machine; Master Deep Belief Networks. Once people have a chance to learn how deep learning can impact their data analysis efforts, they want to get hands on the tools. This book will help you to start building smarter applications today using R. 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 analytics, neural networks and decision science.--Back cover.