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Hands-On Deep Learning with R

You're reading from   Hands-On Deep Learning with R A practical guide to designing, building, and improving neural network models using R

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Product type Paperback
Published in Apr 2020
Publisher Packt
ISBN-13 9781788996839
Length 330 pages
Edition 1st Edition
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Authors (2):
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Rodger Devine Rodger Devine
Author Profile Icon Rodger Devine
Rodger Devine
Michael Pawlus Michael Pawlus
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Michael Pawlus
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Toc

Table of Contents (16) Chapters Close

Preface 1. Section 1: Deep Learning Basics
2. Machine Learning Basics FREE CHAPTER 3. Setting Up R for Deep Learning 4. Artificial Neural Networks 5. Section 2: Deep Learning Applications
6. CNNs for Image Recognition 7. Multilayer Perceptron for Signal Detection 8. Neural Collaborative Filtering Using Embeddings 9. Deep Learning for Natural Language Processing 10. Long Short-Term Memory Networks for Stock Forecasting 11. Generative Adversarial Networks for Faces 12. Section 3: Reinforcement Learning
13. Reinforcement Learning for Gaming 14. Deep Q-Learning for Maze Solving 15. Other Books You May Enjoy

Summary

Having completed this chapter, you should now have all of the libraries that will be used in this book installed. In addition, you should be familiar with the syntax for each of them, and you should have seen a preliminary example of how to train a model using each one. We also explored some of the differences between the deep learning libraries, noting their strengths as well as their limitations. The three main packages (Keras, MXNet, and H2O) are widely used for deep learning in industry and academia, and an understanding of these will enable you to tackle a number of deep learning problems. We are now ready to explore them all in more depth. However, before we do, we will review neural networks—the building block for all deep learning.

In the following chapter, you will learn about artificial neural networks, which comprise the base building block...

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