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Introduction to Deep Learning Using R

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

  • Taweh Beysolow II

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

  • Taweh Beysolow II
    • 1
  1. 1.San FranciscoUSA

Bibliographic information