Deep Learning with R 1st Edition
| J. J. Allaire (Author) Find all the books, read about the author, and more. See search results for this author |
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Summary
Deep Learning with R introduces the world of deep learning using the powerful Keras library and its R language interface. The book builds your understanding of deep learning through intuitive explanations and practical examples.
Continue your journey into the world of deep learning with Deep Learning with R in Motion, a practical, hands-on video course available exclusively at Manning.com (www.manning.com/livevideo/deep-learning-with-r-in-motion).
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 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. J. Allaire, this book builds your understanding of deep learning through intuitive explanations and practical examples. 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.
J.J. Allaire is the founder of RStudio and the author of the R interfaces to TensorFlow and Keras.
Table of Contents
PART 1 - FUNDAMENTALS OF DEEP LEARNING
- What is deep learning?
- Before we begin: the mathematical building blocks of neural networks
- Getting started with neural networks
- Fundamentals of machine learning
PART 2 - DEEP LEARNING IN PRACTICE
- Deep learning for computer vision
- Deep learning for text and sequences
- Advanced deep-learning best practices
- Generative deep learning
- Conclusions
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From the Publisher
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| Deep Learning with Python | Deep Learning with R | |
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Editorial Reviews
About the Author
Francois Chollet is the author of Keras, one of the most widely used libraries for deep learning in Python. He has been working with deep neural networks since 2012. Francois is currently doing deep learning research at Google. He blogs about deep learning at blog.keras.io.
J.J. Allaire is the Founder of RStudio and the creator of the RStudio IDE. J.J. is the author of the R interfaces to TensorFlow and Keras.
Product details
- ASIN : 161729554X
- Publisher : Manning Publications; 1st edition (February 13, 2018)
- Language : English
- Paperback : 360 pages
- ISBN-10 : 9781617295546
- ISBN-13 : 978-1617295546
- Item Weight : 1.5 pounds
- Dimensions : 7.38 x 0.8 x 9.25 inches
- Best Sellers Rank: #576,907 in Books (See Top 100 in Books)
- #117 in Natural Language Processing (Books)
- #156 in Machine Theory (Books)
- #171 in Computer Neural Networks
- Customer Reviews:
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La parte inicial es una introducción a la biblioteca Keras, basada sobre todo, en el desarrollo de ejemplos y de algunos comentarios complementarios.
Los ejemplos están bien desarrollados y proporcionan una buena introducción a Keras y a algunas técnicas de Deep Learning. De todos modos, el tema es muy amplio y, aunque el libro es bastante completo, no puede ser exhaustivo y tampoco explica todas las funciones de Keras.
Personalmente, hubiera agradecido algo más de detalle en las explicaciones y mas gráficos. Quizá las expresiones matemáticas hubieran ayudado. Se presupone un conocimiento previo de AI (de forma implícita) que puede no estar al alcance de cualquiera. A veces da la impresión que se limita a dar recetas.
También es importante indicar que es muy difícil hacer los ejemplos propuestos en el libro sin disponer de GPU u ordenadores especializados.
Los últimos capítulos son mas generales. Excelentes en mi opinión, con una vision de las tendencias y limitaciones del Deep learning. Puede ser que, como ocurre en todos los libros, quede obsoleto en poco tiempo, aunque desde luego merecen la pena


