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Deep Studying with R, 2nd Version

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Deep Studying with R, 2nd Version

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At this time we’re happy to announce the launch of Deep Studying with R,
2nd Version
. 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 find out how to get began with deep studying in R, even when
you haven’t any background in arithmetic or knowledge science. The e-book covers:

  • Deep studying from first rules

  • Picture classification and picture segmentation

  • Time sequence forecasting

  • Textual content classification and machine translation

  • Textual content era, neural type switch, and picture era

Solely modest R information is assumed; all the things 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)). Be taught
what a keras Layer is—by implementing one from scratch utilizing solely
base R. Be taught the distinction between batch normalization and layer
normalization, what layer_lstm() does, what occurs whenever you name
match(), and so forth—all via 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 find out how 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
find out how to compose an environment friendly and quick knowledge pipeline, but in addition find out how 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 other ways. Study
embedding layers by implementing a customized
layer_positional_embedding(). Be taught in regards to 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 eventually, a movie-review textual content
generator.

Generative fashions have their very own devoted chapter, overlaying not solely
textual content era, but in addition variational auto encoders (VAE), generative
adversarial networks (GAN), and elegance 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 e-book shuns magic and hand-waving, and as a substitute pulls again the curtain
on each crucial elementary idea wanted to use deep studying.
After working via the fabric within the e-book, you’ll not solely know
find out how 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 Studying with R, Second Version

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Textual content and figures are licensed beneath Artistic Commons Attribution CC BY 4.0. The figures which have been reused from different sources do not fall beneath this license and may be acknowledged by a notice of their caption: “Determine from …”.

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}
}

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