### Deep Learning with R, Second Edition

#### Francois Chollet, Tomasz Kalinowski, J. J. Allaire

#### $43.99

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### Description

**Deep learning from the ground up using R and the powerful Keras library!**

In *Deep Learning with R, Second Edition* you will learn:

Deep learning from first principles

Image classification and image segmentation

Time series forecasting

Text classification and machine translation

Text generation, neural style transfer, and image generation

*Deep Learning with R, Second Edition* shows you how to put deep learning into action. It’s based on the revised new edition of François Chollet’s bestselling *Deep Learning with Python*. All code and examples have been expertly translated to the R language by Tomasz Kalinowski, who maintains the Keras and Tensorflow R packages at RStudio. Novices and experienced ML practitioners will love the expert insights, practical techniques, and important theory for building neural networks.

About the technology

Deep learning has become essential knowledge for data scientists, researchers, and software developers. The R language APIs for Keras and TensorFlow put deep learning within reach for all R users, even if they have no experience with advanced machine learning or neural networks. This book shows you how to get started on core DL tasks like computer vision, natural language processing, and more using R.

About the book

*Deep Learning with R, Second Edition* is a hands-on guide to deep learning using the R language. As you move through this book, you’ll quickly lock in the foundational ideas of deep learning. The intuitive explanations, crisp illustrations, and clear examples guide you through core DL skills like image processing and text manipulation, and even advanced features like transformers. This revised and expanded new edition is adapted from *Deep Learning with Python, Second Edition* by François Chollet, the creator of the Keras library.

What's inside

Image classification and image segmentation

Time series forecasting

Text classification and machine translation

Text generation, neural style transfer, and image generation

About the reader

For readers with intermediate R skills. No previous experience with Keras, TensorFlow, or deep learning is required.

About the author

**François Chollet** is a software engineer at Google and creator of Keras. **Tomasz Kalinowski** is a software engineer at RStudio and maintainer of the Keras and Tensorflow R packages. **J.J. Allaire** is the founder of RStudio, and the author of the first edition of this book.

Table of Contents

1 What is deep learning?

2 The mathematical building blocks of neural networks

3 Introduction to Keras and TensorFlow

4 Getting started with neural networks: Classification and regression

5 Fundamentals of machine learning

6 The universal workflow of machine learning

7 Working with Keras: A deep dive

8 Introduction to deep learning for computer vision

9 Advanced deep learning for computer vision

10 Deep learning for time series

11 Deep learning for text

12 Generative deep learning

13 Best practices for the real world

14 Conclusions