This hands-on guide teaches deep learning with R and the Keras library, starting from first principles. It is adapted from the second edition of François Chollet’s Deep Learning with Python, with all code and examples translated to R by Tomasz Kalinowski, who maintains the Keras and TensorFlow R packages. J.J. Allaire, who co-wrote the first edition, is also a co-author.
The book is written for readers with intermediate R skills and assumes no prior experience with Keras, TensorFlow, or deep learning. Intuitive explanations, clear illustrations, and worked examples build up the foundations step by step before moving on to core tasks in computer vision and natural language processing, and to more advanced techniques such as transformers. A newer third edition, built on Keras 3, is also available, and the two editions cover partly different material.
Along the way, you’ll learn about:
- Deep learning from first principles
- Image classification and image segmentation
- Time series forecasting
- Text classification and machine translation
- Transformers for natural language processing
- Text generation, neural style transfer, and image generation
The source code for the book’s examples is available on GitHub.
