Deep Learning with R introduces the world of deep learning using the powerful Keras library and its R language interface. The book builds your understanding of deep learning through intuitive explanations and practical examples.
About the Technology
Machine learning has made remarkable progress in recent years. Deep-learning systems now enable previously impossible smart applications, revolutionizing image recognition and natural-language processing, and identifying complex patterns in data. The Keras deep-learning library provides data scientists and developers working in R a state-of-the-art toolset for tackling deep-learning tasks.
About the Book
Deep Learning with R introduces the world of deep learning using the powerful Keras library and its R language interface. Initially written for Python as Deep Learning with Python by Keras creator and Google AI researcher François Chollet and adapted for R by RStudio founder J. J. Allaire, this book builds your understanding of deep learning through intuitive explanations and practical examples. You'll practice your new skills with R-based applications in computer vision, natural-language processing, and generative models.
- Deep learning from first principles
- Setting up your own deep-learning environment
- Image classification and generation
- Deep learning for text and sequences
About the Reader
You'll need intermediate R programming skills. No previous experience with machine learning or deep learning is assumed.
About the Authors
François Chollet is a deep-learning researcher at Google and the author of the Keras library.
J.J. Allaire is the founder of RStudio and the author of the R interfaces to TensorFlow and Keras.
Table of Contents
PART 1 - FUNDAMENTALS OF DEEP LEARNING
Chapter 1. What Is Deep Learning?
Chapter Before We Begin: The Mathematical Building Blocks Of Neural Networks
Chapter Getting Started With Neural Networks
Chapter Fundamentals Of Machine Learning
PART 2 - DEEP LEARNING IN PRACTICE
Chapter 1. Deep Learning For Computer Vision
Chapter 2. Deep Learning For Text And Sequences
Chapter 3. Advanced Deep-Learning Best Practices
Chapter 4. Generative Deep Learning
Chapter 5. Conclusions