Free Ebook Introduction to Deep Learning Using R: A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R

Free Ebook Introduction to Deep Learning Using R: A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R

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Introduction to Deep Learning Using R: A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R

Free Ebook Introduction to Deep Learning Using R: A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R

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From the Back Cover

Understand deep learning, the nuances of its different models, and where these models can be applied.The abundance of data and demand for superior products/services have driven the development of advanced computer science techniques, among them image and speech recognition. Introduction to Deep Learning Using R provides a theoretical and practical understanding of the models that perform these tasks by building upon the fundamentals of data science through machine learning and deep learning. This step-by-step guide will help you understand the disciplines so that you can apply the methodology in a variety of contexts. All examples are taught in the R statistical language, allowing students and professionals to implement these techniques using open source tools.What You Will Learn:• Understand the intuition and mathematics that power deep learning models• Utilize various algorithms using the R programming language and its packages• Use best practices for experimental design and variable selection• Practice the methodology to approach and effectively solve problems as a data scientist• Evaluate the effectiveness of algorithmic solutions and enhance their predictive power

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About the Author

Taweh Beysolow II is a Machine Learning Scientist currently based in the United States with a passion for research and applying machine learning methods to solve problems. He has a Bachelor of Science degree in Economics from St. Johns University and a Master of Science in Applied Statistics from Fordham University. Currently, he is extremely passionate about all matters related to machine learning, data science, quantitative finance, and economics.

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Product details

Paperback: 227 pages

Publisher: Apress; 1st ed. edition (July 20, 2017)

Language: English

ISBN-10: 1484227336

ISBN-13: 978-1484227336

Product Dimensions:

6.1 x 0.6 x 9.2 inches

Shipping Weight: 14.9 ounces (View shipping rates and policies)

Average Customer Review:

2.9 out of 5 stars

8 customer reviews

Amazon Best Sellers Rank:

#1,342,661 in Books (See Top 100 in Books)

Well, I made it to page 30 before I gave up. And the last ten pages I read with a morbid fascination more than interest in the book...Chapter 1 is an introduction and you cannot really get that wrong. The author tried, by sticking to a passive voice and overly complicated descriptions of relatively simple concept, but the style is a matter of taste, so I won't complain too much about that. Chapter 2, however, is an overview of mathematical concepts needed for the rest of the book, and you certainly *can* objectively see when that is just plain wrong. And boy is it. Particularly the description of linear algebra is, well, worse than wrong. It doesn't make sense anywhere. He can't even define vector addition and subtraction correctly. And gods know what he is trying to do when he writes about matrix multiplication, 'cause there are obviously several versions of that in place and only a few places the kind that a linear algebra textbook would teach you. Hell, if he had taken the "using R" from the title serious and actually tried to do any of the computations he uses as examples in R, he would find that mostly the dimensions of the matrices he multiplies are wrong, and when not, the output from the correct answer has no relation to the answers in the book.I give up now.If practically every bit of math that I understand is incorrect in the book, I see no reason to believe that anything that might be new to me would be correct.

The e-book is very poorly formatted, with all the formulas extremely small and they do not zoom in with the text. There are so many obvious typos in both explanatory paragraphs and even technical terms (the author even used wrong abbreviations for the model he was talking about at one point). The codes for Ch5 on GitHub only have 7 lines, it seems that the author deleted the rest by accident. The book was written in 2017, but not many new packages are introduced. Plus, the first example in Ch10 is predicting stock prices, which we know is not predictable. Last but not least, the book is subtitled implementation of DL models using R, but about 80% of it was on theories and math. While math is important, I would have bought the classic DL theory book if I wanted to read about math.

The author wrote the book for people who already have programming knowledge. I dont have programming knowledge or advance mathematics skills so the book is difficult for me to comprehend. Also there is no example or problem with answers to help or enhance learning.I will return this book.

The book covers many topics in machine learning in an extremely superficial way. Most of the book is not about deep learning and the inclusion of R scripts is not adequate. The mathematics underpinning of methods is totally lacking. It's better just pull out a few review papers in the field rather than reading this book. The title of the book is misleading. The content is exceptionally shallow. Avoid.

A solid review of introductory methods of ML/DL for those who are entering the field with some background in mathematics and programming. The jargon and abstractness can catch some readers (including myself) but it can be a good reference in your library.

While subjects such as statistics and deep learning are not intuitive, Beysolow demonstrates his prowess in a multitude subjects and covers a lot of dense material. He accomplishes this in such a way that allows individuals who are relatively new to the vast subjects have a very good foot to understanding all the topics covered including: A/B testing, different types of regression modeling, Deep Belief Networks, along with other important Machine Learning such as Neural Networks. It is an excellent book to learn from or reference while performing and troubleshooting any of the modeling techniques with R. I am extremely impressed and am thankful to have been able to read and apply several of these models that I initially did not use prior.If anyone is interested in learning how to use R and understanding the world of Deep Learning, this book would be a step in the right direction.

Well worth it. What stood out the most was the organization of topics and presentation of a deep (no pun intended) topic from a ground-floor-up perspective. Before reading the book, my familiarity with deep learning amounted to having read a few blog posts. With intermediate knowledge of R and having finished this book, I feel confident with writing and training several deep learning models, and have a great foundation for further expanding my knowledge on the topic. Thanks for your awesome work!

This book covers a lot of ground. The author lays out all the tools he will need at the start of the book - a review of all the relevant statistical concepts, a thorough explanation of machine learning, and introduction to neural networks - and uses them with care and precision to lay out a thorough grounding in Deep Learning. While it's intellectually pretty heavy-weight, I think Beysolow sells himself short when he suggests that the reader should have some decent familiarity with the R programming language and statistics. As someone who doesn't know R and whose statistics knowledge was pretty rusty, the methodical, I found that the step-by-step introduction of new concepts meant that there was plenty for me in this book (even if I'm sure some of the nuance was lost on me).An excellent introduction to one of the most important fields in computer science today.

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