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Introduction to Deep Learning Using R: A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R
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'll 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
Who This Book Is For
Students, researchers, and data scientists who are familiar with programming using R. This book also is also of use for those who wish to learn how to appropriately deploy these algorithms in applications where they would be most useful.
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'll 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
Who This Book Is For
Students, researchers, and data scientists who are familiar with programming using R. This book also is also of use for those who wish to learn how to appropriately deploy these algorithms in applications where they would be most useful.
246 pages, Paperback
Published July 20, 2017
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Displaying 1 - 2 of 2 reviews
March 30, 2018
This is probably the worst textbook I have ever had the displeasure of reading, and I gave up on page 30. It is just awful. The language is horrible and the math is plain and simply wrong more often than not. If you write about linear algebra and you are incapable of defining addition and subtraction for vectors or multiplication on matrices -- and your examples would never make it through a computer, despite the title mentioning a language that does support linear algebra, because you get the dimensions wrong in more than half the cases -- then you just shouldn't be writing about linear algebra.
No, this book is just horrible.
I don't trust for a second that any of the material that might be new to me in later chapters would be correct if all the simple stuff, that I do know quite well, is wrong more often than not.
If you are interested in a laugh, though, please do read the mathematical review in chapter 2. I'll sell you my copy of the book if you are quick. But you have to be quick because at some point I will be out of kindling and then I can find a use for this piece of...
No, this book is just horrible.
I don't trust for a second that any of the material that might be new to me in later chapters would be correct if all the simple stuff, that I do know quite well, is wrong more often than not.
If you are interested in a laugh, though, please do read the mathematical review in chapter 2. I'll sell you my copy of the book if you are quick. But you have to be quick because at some point I will be out of kindling and then I can find a use for this piece of...
June 6, 2018
Although the book covers different good methods, the language and organization of the book is not well and there are several mistakes in formulae.
Displaying 1 - 2 of 2 reviews