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Deep Learning with R

Amazon.com Price:  $47.49 (as of 22/04/2019 22:25 PST- Details)

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Description

Summary

Deep Learning with R introduces the world of deep learning the use of the powerful Keras library and its R language interface. The book builds your understanding of deep learning through intuitive explanations and practical examples.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

About the Technology

Machine learning has made remarkable progress in contemporary years. Deep-learning systems now enable prior to now not possible smart applications, revolutionizing image recognition and natural-language processing, and identifying complex patterns in data. The Keras deep-learning library provides data scientists and developers working in R a state of the art toolset for tackling deep-learning tasks.

About the Book

Deep Learning with R introduces the world of deep learning the use of the powerful Keras library and its R language interface. To begin with written for Python as Deep Learning with Python by Keras writer and Google AI researcher François Chollet and adapted for R by RStudio founder J. J. Allaire, this book builds your understanding of deep learning through intuitive explanations and practical examples. You’ll be able to practice your new skills with R-based applications in computer vision, natural-language processing, and generative models.

What’s Inside

  • Deep learning from first principles
  • Setting up your own deep-learning environment
  • Image classification and generation
  • Deep learning for text and sequences

About the Reader

You’ll need intermediate R programming skills. No previous experience with machine learning or deep learning is assumed.

About the Authors

François Chollet is a deep-learning researcher at Google and the writer of the Keras library.

J.J. Allaire is the founder of RStudio and the writer of the R interfaces to TensorFlow and Keras.

Table of Contents

    PART 1 – FUNDAMENTALS OF DEEP LEARNING

  1. What is deep learning?
  2. Before we begin: the mathematical building blocks of neural networks
  3. Getting began with neural networks
  4. Fundamentals of machine learning

    5. PART 2 – DEEP LEARNING IN PRACTICE

  5. Deep learning for computer vision
  6. Deep learning for text and sequences
  7. Advanced deep-learning best practices
  8. Generative deep learning
  9. Conclusions

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