August 2018
Intermediate to advanced
378 pages
9h 9m
English
Deep learning is probably the hottest technology in data science right now, and R is one of the most popular data science languages. However, R is not considered as an option for deep learning by many people, which is a shame, as R is a wonderful language for data science. This book shows that R is a viable option for deep learning, because it supports libraries such as MXNet and Keras.
When I decided to write this book, I had numerous goals. First, I wanted to show how to apply deep learning to various tasks, and not just to computer vision and natural language processing. This book covers those topics, but it also shows how to use deep learning for prediction, regression, anomaly detection, and recommendation systems. The second ...