Introduction to Deep Learning Using R is published by Apress in July 2017. This book has 227 pages in English, ISBN-13 978-1484227336.
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