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Java Deep Learning Cookbook

You're reading from   Java Deep Learning Cookbook Train neural networks for classification, NLP, and reinforcement learning using Deeplearning4j

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Product type Paperback
Published in Nov 2019
Publisher Packt
ISBN-13 9781788995207
Length 304 pages
Edition 1st Edition
Languages
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Author (1):
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Rahul Raj Rahul Raj
Author Profile Icon Rahul Raj
Rahul Raj
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Toc

Table of Contents (14) Chapters Close

Preface 1. Introduction to Deep Learning in Java 2. Data Extraction, Transformation, and Loading FREE CHAPTER 3. Building Deep Neural Networks for Binary Classification 4. Building Convolutional Neural Networks 5. Implementing Natural Language Processing 6. Constructing an LSTM Network for Time Series 7. Constructing an LSTM Neural Network for Sequence Classification 8. Performing Anomaly Detection on Unsupervised Data 9. Using RL4J for Reinforcement Learning 10. Developing Applications in a Distributed Environment 11. Applying Transfer Learning to Network Models 12. Benchmarking and Neural Network Optimization 13. Other Books You May Enjoy

Designing hidden layers for the neural network model

Hidden layers are the heart of a neural network. The actual decision process happens there. The design of the hidden layers is based on hitting a level beyond which a neural network cannot be optimized further. This level can be defined as the optimal number of hidden layers that produce optimal results.

Hidden layers are the place where the neural network transforms the inputs into a different format that the output layer can consume and use to make predictions. In this recipe, we will design hidden layers for a neural network.

How to do it...

  1. Determine the incoming/outgoing connections. Set the following:
incoming neurons = outgoing neurons from preceding layer.
outgoing...
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