Deep Studying with R, 2nd Version

At the moment we’re happy to announce the launch of Deep Learning with R,
2nd Edition. In comparison with the primary version,
the ebook is over a 3rd longer, with greater than 75% new content material. It’s
not a lot an up to date version as an entire new ebook.

This ebook reveals you the right way to get began with deep studying in R, even when
you haven’t any background in arithmetic or information science. The ebook covers:

• Deep studying from first rules

• Picture classification and picture segmentation

• Time sequence forecasting

• Textual content classification and machine translation

• Textual content technology, neural model switch, and picture technology

Solely modest R information is assumed; every part else is defined from
the bottom up with examples that plainly reveal the mechanics.
Find out about gradients and backpropogation—through the use of tf$GradientTape()
to rediscover Earth’s gravity acceleration fixed (9.8 (m/s^2)). Study
what a keras Layer is—by implementing one from scratch utilizing solely
base R. Study the distinction between batch normalization and layer
normalization, what layer_lstm() does, what occurs whenever you name
match(), and so forth—all by means of implementations in plain R code.

Each part within the ebook has acquired main updates. The chapters on
laptop imaginative and prescient achieve a full walk-through of the right way to strategy a picture
segmentation process. Sections on picture classification have been up to date to
use {tfdatasets} and Keras preprocessing layers, demonstrating not simply
the right way to compose an environment friendly and quick information pipeline, but in addition the right way to
adapt it when your dataset requires it.

The chapters on textual content fashions have been utterly reworked. Learn to
preprocess uncooked textual content for deep studying, first by implementing a textual content
vectorization layer utilizing solely base R, earlier than utilizing
keras::layer_text_vectorization() in 9 other ways. Find out about
embedding layers by implementing a customized
layer_positional_embedding(). Study concerning the transformer structure
by implementing a customized layer_transformer_encoder() and
layer_transformer_decoder(). And alongside the way in which put all of it collectively by
coaching textual content fashions—first, a movie-review sentiment classifier, then,
an English-to-Spanish translator, and at last, a movie-review textual content
generator.

Generative fashions have their very own devoted chapter, protecting not solely
textual content technology, but in addition variational auto encoders (VAE), generative
adversarial networks (GAN), and magnificence switch.

Alongside every step of the way in which, you’ll discover sprinkled intuitions distilled
from expertise and empirical remark about what works, what
doesn’t, and why. Solutions to questions like: when do you have to use
bag-of-words as a substitute of a sequence structure? When is it higher to
use a pretrained mannequin as a substitute of coaching a mannequin from scratch? When
do you have to use GRU as a substitute of LSTM? When is it higher to make use of separable
convolution as a substitute of standard convolution? When coaching is unstable,
what troubleshooting steps do you have to take? What are you able to do to make
coaching quicker?

The ebook shuns magic and hand-waving, and as a substitute pulls again the curtain
on each needed elementary idea wanted to use deep studying.
After working by means of the fabric within the ebook, you’ll not solely know
the right way to apply deep studying to frequent duties, but in addition have the context to
go and apply deep studying to new domains and new issues.

Deep Learning with R, Second Edition

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Quotation

For attribution, please cite this work as

Kalinowski (2022, Could 31). Posit AI Weblog: Deep Studying with R, 2nd Version. Retrieved from https://blogs.rstudio.com/tensorflow/posts/2022-05-31-deep-learning-with-R-2e/

BibTeX quotation

@misc{kalinowskiDLwR2e,
  creator = {Kalinowski, Tomasz},
  title = {Posit AI Weblog: Deep Studying with R, 2nd Version},
  url = {https://blogs.rstudio.com/tensorflow/posts/2022-05-31-deep-learning-with-R-2e/},
  yr = {2022}
}