Deep Learning with R [Book]
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PRODUCT DETAILS
Format
: Paperback
Type
: Fiction
Genre
: Computer
Authors
: Francois Chollet, J.J. Allaire
Features
: Manning, paperback
Summary - Deep Learning with R introduces the world of deep learning using the powerful Keras library and its R language interface. Purchase of the print book includes a free eBook in PDF, Kindle, and e - Pub formats from Manning Publications. - About the Technology - Machine learning has made remarkable progress in recent years. Deep-learning systems now enable previously impossible 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 using the powerful Keras library and its R language interface. Initially written for Python as Deep Learning with Python by Keras creator and Google AI researcher François Chollet and adapted for R by RStudio founder J. You'll 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 author of the Keras library. Allaire is the founder of RStudio and the author of the R interfaces to Tensor - Flow and Keras. - Table of Contents - PART 1 - FUNDAMENTALS OF DEEP LEARNINGWhat is deep learning? - Getting started with neural networks - Fundamentals of machine learning - PART 2 - DEEP LEARNING IN PRACTICEDeep learning for computer vision - Deep learning for text and sequences - Advanced deep-learning best practices - Generative deep learning - Conclusions
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