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© 2017

Introduction to Deep Learning Using R

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

Book

Table of contents

  1. Front Matter
    Pages i-xix
  2. Taweh Beysolow II
    Pages 1-9
  3. Taweh Beysolow II
    Pages 11-43
  4. Taweh Beysolow II
    Pages 45-87
  5. Taweh Beysolow II
    Pages 89-100
  6. Taweh Beysolow II
    Pages 101-112
  7. Taweh Beysolow II
    Pages 113-124
  8. Taweh Beysolow II
    Pages 137-166
  9. Taweh Beysolow II
    Pages 167-170
  10. Taweh Beysolow II
    Pages 171-194
  11. Taweh Beysolow II
    Pages 195-218
  12. Taweh Beysolow II
    Pages 219-220
  13. Back Matter
    Pages 221-227

About this book

Introduction

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

Keywords

Deep Learning R Single Layer Artificial Neural Networks Deep Neural Networks Convolutional Neural Networks Recurrent Neural networks Deep Belief Networks Deep Boltzman Machines Deep Network Architecture Statistics

Authors and affiliations

  1. 1.San FranciscoUSA

About the authors

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.

Bibliographic information

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