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R Deep Learning Essentials. - Second Edition

You're reading from  R Deep Learning Essentials. - Second Edition

Product type Book
Published in Aug 2018
Publisher Packt
ISBN-13 9781788992893
Pages 378 pages
Edition 2nd Edition
Languages
Authors (2):
Mark Hodnett Mark Hodnett
Profile icon Mark Hodnett
Joshua F. Wiley Joshua F. Wiley
Profile icon Joshua F. Wiley
View More author details
Toc

Table of Contents (13) Chapters close

Preface 1. Getting Started with Deep Learning 2. Training a Prediction Model 3. Deep Learning Fundamentals 4. Training Deep Prediction Models 5. Image Classification Using Convolutional Neural Networks 6. Tuning and Optimizing Models 7. Natural Language Processing Using Deep Learning 8. Deep Learning Models Using TensorFlow in R 9. Anomaly Detection and Recommendation Systems 10. Running Deep Learning Models in the Cloud 11. The Next Level in Deep Learning 12. Other Books You May Enjoy

Additional deep learning resources

This section consists of some recommendations if you wish to continue your development in deep learning. My first recommendation is that you ensure that you have run all the code examples in this book. You are getting less than 50% of the benefit if you just read this book without running the code. Go through the examples, change the code to try and beat the results I got, re-write the MXNet code to Keras code, and so on.

I strongly recommend Deep Learning Specialization on Coursera by Andrew Ng (https://www.coursera.org/specializations/deep-learning). Unfortunately, it is in Python, but it still is a great resource. Also in Python are two excellent courses on fast.ai (http://www.fast.ai/) from Jeremy Howard. These two options take opposite approaches: the Deep Learning specialization on Coursera takes a bottom-up approach that goes from theory...

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