Introduction to Deep Learning Using R

Introduction to Deep Learning Using R

Book Description
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.


Chapter 1: Introduction to Deep Learning 
Chapter 2: Mathematical Review 
Chapter 3: A Review of Optimization and Machine Learning 
Chapter 4: Single and Multilayer Perceptron Models 
Chapter 5: Convolutional Neural Networks (CNNs) 
Chapter 6: Recurrent Neural Networks (RNNs)
Chapter 7: Autoencoders, Restricted Boltzmann Machines, and Deep Belief Networks 
Chapter 8: Experimental Design and Heuristics 
Chapter 9: Hardware and Software Suggestions 
Chapter 10: Machine Learning Example Problems 
Chapter 11: Deep Learning and Other Example Problems 
Chapter 12: Closing Statements 

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