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Master Deep Learning & Leverage Business Analytics – the Easy Way! Deep Learning for Business With R takes you on a gentle, fun and unhurried journey to building your own deep neural network models for business use in R. Using plain language, it offers an intuitive, practical, non-mathematical, easy to follow guide to the most successful ideas, outstanding techniques and usable solutions available using R.
BUSINESS ANALYTICS FAST! This book is an ideal introduction to deep learning for business analytics. It is designed to be accessible. It will teach you, in simple and easy-to-understand terms, how to take advantage of deep learning to enhance business outcomes.
NO EXPERIENCE REQUIRED I’m assuming you never did like linear algebra, don’t want to see things derived, dislike complicated computer code, and you’re here because you want to see how to use deep neural networks for business problems explained in plain language, and try them out for yourself.
THIS BOOK IS FOR YOU IF YOU WANT:
QUICK AND EASY: Deep Learning is little more than using straight-forward steps to process data into actionable insight. And in Deep Learning for Business with R, author Dr. N.D Lewis will show you how that's done. It's easier than you think. Through a simple to follow process you will learn how to build deep neural network models for business problems in R. Once you have mastered the process, it will be easy for you to translate your knowledge into your own powerful business applications.
TAKE THE SHORTCUT: R is easy to use, available on all major operating systems and free! Each chapter covers, step by step, a different aspect of deep neural networks. You get your hands dirty as you work through some challenging real world business issues.
YOU'LL LEARN HOW TO:
ACCELERATE YOUR PROGRESS If you want to accelerate your progress and act on what you have learned, this book is the place to get started. It reveals how deep neural networks 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 and free R programming language.
Everything you need to get started is contained within this book. Deep Learning for Business With R is your very own hands on practical, tactical, easy to follow guide to mastery
Buy this book today your next big breakthrough using deep neural networks is only a page away!
- Sales Rank: #589743 in Books
- Published on: 2016-08-31
- Original language: English
- Dimensions: 9.00" h x .58" w x 6.00" l,
- Binding: Paperback
- 254 pages
Most helpful customer reviews
4 of 4 people found the following review helpful.
3 for Content 5 for author’s support
By Abacus
The book is reasonably good. It explains Deep Learning fairly well in a manner that is digestible for the non-PhD (notice the topic is very technical by nature, so it does cater to an audience that has a good understanding of quantitative methods in general). A definite positive associated with this book is that the author gives you exposure and teaches you how to use three different R Deep Learning packages.
However, the narrative includes a few confusing statements. For instance, the author advances that Deep Neural Networks (DNNs) are the equivalent of a series of Log-linear regression models. That’s inaccurate. He meant to say Logit Regression models instead. Also, throughout the book he repeats that it is critical to standardize or scale the variables (using a Min/Max scaling) so as to render them Normally distributed. However, those procedures will not change the shape of the variable. They will only change its scale. So, this argument is not appropriate. I passed my criticism by the author, and he very graciously accepted them.
When it comes to the examples, they are interesting and well explained. But, they are not always replicable. I worked on a couple of chapters. And, at times I derived the same results at other times I got very different results with the same coding as his. I know DNNs can be unstable. So, that may explain some divergence in replication. However, we both started those models with the same seed number embedded in our codes. So, the divergence is puzzling. In one interesting case, he developed a DNN with a reasonably high R Square only to uncover that the underlying correlation between the model’s estimate and the response variable was negative. This seemed mathematically improbable. When I ran the exact same model, I got a much lower R Square than he did. However, the correlation was positive as one would expect.
The author generates much goodwill with his readership. He is passionate about educating and sharing his knowledge. He is readily accessible. He actually helped me out with my coding in one case and very gladly accepted my debating some of the mentioned concepts with him.
1 of 1 people found the following review helpful.
Best of breed on the subject matter
By OHYN
Easy reading. Practical info. I built models that work following the books step by step instructions. The references are fantastic too.
The author is easy to approach and continues to be helpful in answering questions related to the topics with rich resources.
I also got the Kindle version so I have a pocket workbook and reference on my smart phone.
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