Deep Learning with R

SummaryDeep 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. Continue your journey into the world of deep learning with Deep Learning with R in Motion, a practical, hands-on video course available exclusively at Manning. com (www. manning. com/livevideo/deep-​ learning-with-r-in-motion). Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the TechnologyMachine 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 BookDeep 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. What's Inside

• Deep learning from first principles
• Setting up your own deep-learning environment
• Image classification and generation
• Deep learning for text and sequences

About the ReaderYou'll need intermediate R programming skills. No previous experience with machine learning or deep learning is assumed. About the AuthorsFrançois 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
1. What is deep learning?
2. Before we begin: the mathematical building blocks of neural networks
3. Getting started with neural networks
4. Fundamentals of machine learning
PART 2 - DEEP LEARNING IN PRACTICE
5. Deep learning for computer vision
6. Deep learning for text and sequences
7. Advanced deep-learning best practices
8. Generative deep learning
9. Conclusions