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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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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

Exploring ReinforcementLearning and RBM

The ReinforcementLearning and RBM packages differ from the libraries already covered in two important ways: first, they are specialized packages that have functions for only one specific deep learning task instead of attempting to support myriad deep learning options, and second, they are completely written in R and have no additional language dependencies. This can be an advantage as the complexity of the previous libraries means that the packages can break when changes happen outside the package. The support pages for these libraries are full of examples of installation FAQs and troubleshooting instructions, as well as some cases where a given package may suddenly stop working or become deprecated. In these cases, we encourage you to continue searching CRAN and other sites as the R community is well known for its dynamic, evolving, and...

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