Kniha Deep Learning with R, Second Edition Tomasz Kalinowski

Deep Learning with R, Second Edition

Jazyk: Angličtina
Vazba: Brožovaná
Dostupnost: 50 % šance
Prohledáme celý svět
1 447
Deep learning from the ground up using R and the powerful Keras library! In   Deep Learning with...

Informace o knize

Jazyk
Angličtina
Vazba
Kniha - Brožovaná
Vydáno
2022
Stránek
568
EAN
9781633439849
ISBN
1633439844
Enbook ID
39332923
Hmotnost
1022
Rozměry
235 x 190 x 39

Kompletní popis

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.

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.
 

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