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Neural Networks with R

You're reading from   Neural Networks with R Build smart systems by implementing popular deep learning models in R

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Product type Paperback
Published in Sep 2017
Publisher Packt
ISBN-13 9781788397872
Length 270 pages
Edition 1st Edition
Languages
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Authors (2):
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Balaji Venkateswaran Balaji Venkateswaran
Author Profile Icon Balaji Venkateswaran
Balaji Venkateswaran
Giuseppe Ciaburro Giuseppe Ciaburro
Author Profile Icon Giuseppe Ciaburro
Giuseppe Ciaburro
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Toc

Table of Contents (8) Chapters Close

Preface 1. Neural Network and Artificial Intelligence Concepts 2. Learning Process in Neural Networks FREE CHAPTER 3. Deep Learning Using Multilayer Neural Networks 4. Perceptron Neural Network Modeling – Basic Models 5. Training and Visualizing a Neural Network in R 6. Recurrent and Convolutional Neural Networks 7. Use Cases of Neural Networks – Advanced Topics

Best practices in neural network implementations

The following are some best practices that will help in the implementation of neural network:

  • Neural networks are best implemented when there is good training data
  • More the hidden layers in an MLP, the better the accuracy of the model for predictions
  • It is best to have five nodes in the hidden layer
  • ReLU and Sum of Square of Errors (SSE) are respectively best techniques for activation function and error deduction
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