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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
Author Profile Icon Michael Pawlus
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

Deciding on the hidden layers and neurons

Multilayer perceptrons provide only a few choices during the model design process: the activation function used in the hidden layers, the number of hidden layers, and the number of nodes or artificial neurons in each layer. The topic of selecting the optimal number of layers and nodes will be covered in this section. We can begin with a single layer and use a set of heuristics to guide our starting point for selecting the number of nodes to include in this hidden layer.

When beginning this process, a good starting point is 66% of the length of the input or the number of independent variable columns. This value, in general, will fall within a range between the size of the output to two times the size of the input; however, 66% of the length of the input is a good starting point within this range.

This does not mean that this starting...

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