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