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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 Netwo
DEEP LEARNING MADE EASY ⅵIT五R A Gentle Introduction for Data Science Dr. N.D. Lewis Copyright o 2016 by N.D. Lewis All rights reserved. No part of this publication may be reproduced, dis tributed, or transmitted in any form or by any means, including photo- copying, recording, or other electronic or mechanical methods, without the prior written permission of the author, except in the case of brief quo tations embodied in critical reviews and certain other noncommercial uses permitted by copyright law. For permission requests, contact the author Disclaimer: Although the author and publisher have made every effort to ensure that the information in this book was correct at press time, the author and publisher do not assume and hereby disclaim any liability to any party for any loss, damage, or disruption caused by errors or omissions whether such errors or omissions result from negligence, accident, or any other cause Ordering Information: Quantity sales. Special discounts are available on quantity purchases by corporations, associations, and others. For details email: info@NigelDLewis com Image photography by Deanna Lewis ISBN:978-1519514219 ISBN:1519514212 Contents Acknowledgements Preface How to get the most from this book 1 Introduction What is Deep Learning 6 What Problems Can Deep Learning Solve? Who Uses Deep Learning? 9 A Primer on neural Networks Notes 24 Neural networks 31 The Amazingly Simple anatomy of the DNN 32 How to explain a dnn in 60 Seconds or Less .33 Three brilliant Ways to Use Deep Neural Networks 34 The AbCs of choosing the Optimal Number of layers How to Imnediately Approximate Any Function 42 The Answer to How Many Neurons to Include 48 49 Three Ideas to Supercharge DNn Performance 51 Incredibly Simple Ways to Build DNNs with R 57 83 3 Elman neural networks 87 What is an Elman Neural Network? What is the role of context Laver neurons 88 How to understand the Information Flow 89 How to use elman networks to boost Your result's 91 Four Smart Ways to use Elman Neural Networks 92 The Easy Way to Build Elman Networks 9 Here is how to load the best packages 9 Why viewing Data is the New science 96 The Secret to Transforming data 100 How to Estimate an Interesting Model 102 Creating the Ideal prediction 104 .106 4 Jordan neural networks 107 Three Problems jordan Neural Networks Can Solve .108 EsSential Elements for Effective jordan models in r 110 Which are the Appropriate Packages? 110 a Dann Good way to Transform Dat 112 Here is How to Select the Training Sample 114 Use This Tip to Estimate Your Model 115 Notes 117 5 The secret to the Autoencoder 119 A Jedi mind trick 120 A Practical Definition You Can Run With The secret Revealed 121 124 Saving the brazilian Cerrado 124 The Essential ingredient You Need to Know 125 The Powerful Benefit of the Sparse Autoencoder 126 Understanding Kullback-Leibler Divergence 126 Three Timeless Lessons from the Sparse Autoencoder 128 Mixing Hollywood, Biometrics Sparse Autoencoders ,128 How to Imnediately use the Autoencoder in R An Idea for Your Own Data Science Projects with R 131 137 Notes 145 6 The Stacked Autoencoder in a Nutshell 147 The Deep Learning Gurus Secret Sauce for Training 148 How Much Sleep do You need? 149 What is a Denoising Autoencoder? S Than 5 Minutes Build a stacked autoencoder in 153 153 The Salt and Pepper of Random Masking 156 The Two Essential Tasks of a Denoising Autoencoder 156 How to Understand stacked Denoising Autoencoders 157 A Stunningly Practical Application 158 The Fast Path to a Denoising Autoencoder in R 166 Notes ,,,,174 7 Restricted boltzmann machines 177 he Four Steps to knowledge 177 The Role of Energy and Probability 179 a Magnificent Way to Think 181 The Goal of Model Learning 182 Training Tricks that Work like Magic .183 The Key Criticism of Deep Learning 187 Two Ideas that can Change the World 188 Secrets to the Restricted Boltzmann Machine in R 194 201 8 Deep Belief Networks 205 How to Train a Deep Belief Network 206 How to deliver a Better Call Waiting experience 207 a World First Idea that You Can easily emulate 209 Steps to Building a Deep Belief Network in R 12 215 Index 222 Dedicated to Angela, wife, friend and mother extraordinaire Acknowledgments A special thank you to My wife Angela, for her patience and constant encouragement My daughter deanna, for taking hundreds of photographs for this book and my website And the readers of my earlier books who contacted me with questions and suggestions

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caoylo 说不错,对R在机器学习上的应用解释的很清晰!
ghdqt 非常不错!!!
sinat_24012619 不错 很好 不错 很好
omigax 书是2016年版的,全书主要介绍了一些深度学习算法的r包及其应用。
JamesDeeds 谢谢分享!
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