Deep Learning with R, 2nd Edition

Announcing the release of “Deep Learning with R, 2nd Edition,” a book that shows you how to get started with deep learning in R.

Tomasz Kalinowski (RStudio)https://www.rstudio.com/
2022-05-31

Today we’re pleased to announce the launch of Deep Learning with R, 2nd Edition. Compared to the first edition, the book is over a third longer, with more than 75% new content. It’s not so much an updated edition as a whole new book.

This book shows you how to get started with deep learning in R, even if you have no background in mathematics or data science. The book covers:

• 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

Only modest R knowledge is assumed; everything else is explained from the ground up with examples that plainly demonstrate the mechanics. Learn about gradients and backpropogation—by using tf\$GradientTape() to rediscover Earth’s gravity acceleration constant (9.8 $$m/s^2$$). Learn what a keras Layer is—by implementing one from scratch using only base R. Learn the difference between batch normalization and layer normalization, what layer_lstm() does, what happens when you call fit(), and so on—all through implementations in plain R code.

Every section in the book has received major updates. The chapters on computer vision gain a full walk-through of how to approach an image segmentation task. Sections on image classification have been updated to use {tfdatasets} and Keras preprocessing layers, demonstrating not just how to compose an efficient and fast data pipeline, but also how to adapt it when your dataset calls for it.

The chapters on text models have been completely reworked. Learn how to preprocess raw text for deep learning, first by implementing a text vectorization layer using only base R, before using keras::layer_text_vectorization() in nine different ways. Learn about embedding layers by implementing a custom layer_positional_embedding(). Learn about the transformer architecture by implementing a custom layer_transformer_encoder() and layer_transformer_decoder(). And along the way put it all together by training text models—first, a movie-review sentiment classifier, then, an English-to-Spanish translator, and finally, a movie-review text generator.

Generative models have their own dedicated chapter, covering not only text generation, but also variational auto encoders (VAE), generative adversarial networks (GAN), and style transfer.

Along each step of the way, you’ll find sprinkled intuitions distilled from experience and empirical observation about what works, what doesn’t, and why. Answers to questions like: when should you use bag-of-words instead of a sequence architecture? When is it better to use a pretrained model instead of training a model from scratch? When should you use GRU instead of LSTM? When is it better to use separable convolution instead of regular convolution? When training is unstable, what troubleshooting steps should you take? What can you do to make training faster?

The book shuns magic and hand-waving, and instead pulls back the curtain on every necessary fundamental concept needed to apply deep learning. After working through the material in the book, you will not only know how to apply deep learning to common tasks, but also have the context to go and apply deep learning to new domains and new problems.

Deep Learning with R, Second Edition

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Citation

Kalinowski (2022, May 31). Posit AI Blog: Deep Learning with R, 2nd Edition. Retrieved from https://blogs.rstudio.com/tensorflow/posts/2022-05-31-deep-learning-with-R-2e/
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