图书基本信息

Deep Learning with R
作者： Francois Chollet;J. J. Allaire;
ISBN13： 9781617295546
类型： 平装(简装书)
语种： 英语（English）
出版日期： 2018-02-09
出版社： Manning Publications
页数： 360
重量（克）： 544
尺寸： 23.368 x 18.542 x 1.778 cm

商品简介

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.

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 Francois 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

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