Free Deep Learning Made Easy With R: A Gentle Introduction For Data Science. Ebooks Online
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 NetworksUse AutoencodersUnleash the power of Stacked AutoencodersLeverage the Restricted Boltzmann MachineDevelop Recurrent Neural NetworksMaster Deep Belief NetworksEverything 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!
File Size: 7489 KB
Print Length: 254 pages
Publication Date: January 9, 2016
Sold by: Digital Services LLC
Language: English
ASIN: B01AEXMX34
Text-to-Speech: Enabled
X-Ray: Not Enabled
Word Wise: Not Enabled
Lending: Not Enabled
Enhanced Typesetting: Not Enabled
Best Sellers Rank: #129,539 Paid in Kindle Store (See Top 100 Paid in Kindle Store) #25 in Books > Computers & Technology > Computer Science > AI & Machine Learning > Neural Networks #136 in Books > Computers & Technology > Computer Science > AI & Machine Learning > Intelligence & Semantics #1446 in Kindle Store > Kindle eBooks > Computers & Technology
(Oh wow - did actually remove my original review? That would be a first in my reviewer career. True, the review said that I had not seen the book. Instead, it pointed to unfortunate experience with the author's previous books, and recommended prospective buyers to (a) get the paper version, so that they could return it if needed, and (b) google relevant R packages, as chances were high that this outing of ND Lewis would not add much to the packages' vignettes. I still think that this was useful advice).Anyway, now I have read the book, and my low expectations are confirmed. Apparently, "deep learning" is the new buzzword for neural networks - even more narrowly, multi-layer perceptrons - and "Deep Learning Made Easy With R" is a low-value-added wham-bam job built around "neuralnet" R package. ("deepnet" and "RSNNS" make an appearance too). The author is enthusiastic about the subject, and clearly speaks from experience, but, as before, he just cannot be bothered to proof-read this text - literally the first line of page 1 invites you to "role up" your sleeves - and simply cannot or will not explain things well.His ticket to getting 216 (smallish) pages is to tell you about published neural-nets applications, paper after paper. Jokes, witticisms and pop-culture references abound - then, out of nowhere, you get hit with a tricky formula, a move that screams "Weak writer". Two out of seven-and-a-half chapters - specifically, those dealing with "autoencoder" - are of zero use to 99% of readers, and can just be subtracted from the page count. (People who deal with image compression/decompression read better books/papers, don't they?
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