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

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. Front Matter....Pages i-xix Introduction to Deep Learning....Pages 1-9 Mathematical Review....Pages 11-43 A Review of Optimization and Machine Learning....Pages 45-87 Single and Multilayer Perceptron Models....Pages 89-100 Convolutional Neural Networks (CNNs)....Pages 101-112 Recurrent Neural Networks (RNNs)....Pages 113-124 Autoencoders, Restricted Boltzmann Machines, and Deep Belief Networks....Pages 125-136 Experimental Design and Heuristics....Pages 137-166 Hardware and Software Suggestions....Pages 167-170 Machine Learning Example Problems....Pages 171-194 Deep Learning and Other Example Problems....Pages 195-218 Closing Statements....Pages 219-220 Back Matter....Pages 221-227