Deep Studying with R, 2nd Version

Immediately we’re happy to announce the launch of Deep Learning with R,
2nd Edition. In comparison with the primary version,
the e book 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 e book.

This e book exhibits you the best way to get began with deep studying in R, even when
you don’t have any background in arithmetic or information science. The e book covers:

Deep studying from first ideas
Picture classification and picture segmentation
Time sequence forecasting
Textual content classification and machine translation
Textual content era, neural model switch, and picture era

Solely modest R data is assumed; every thing else is defined from
the bottom up with examples that plainly reveal the mechanics.
Study 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 while you name
match(), and so forth—all by way of implementations in plain R code.

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

The chapters on textual content fashions have been utterly reworked. Discover ways 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 alternative ways. Study
embedding layers by implementing a customized
layer_positional_embedding(). Study in regards to the transformer structure
by implementing a customized layer_transformer_encoder() and
layer_transformer_decoder(). And alongside the best way put all of it collectively by
coaching textual content fashions—first, a movie-review sentiment classifier, then,
an English-to-Spanish translator, and eventually, a movie-review textual content
generator.

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

Alongside every step of the best way, 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 sooner?

The e book shuns magic and hand-waving, and as a substitute pulls again the curtain
on each vital elementary idea wanted to use deep studying.
After working by way of the fabric within the e book, you’ll not solely know
the best way to apply deep studying to widespread duties, but additionally 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, Might 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/},
  12 months = {2022}
}