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Java Deep Learning Projects

You're reading from Java Deep Learning Projects

Product type Book

Published in Jun 2018

Publisher Packt

ISBN-13 9781788997454

Pages 436 pages

Edition 1st Edition

Languages

Concepts

Table of Contents (13) Chapters

Preface

1. Getting Started with Deep Learning

2. Cancer Types Prediction Using Recurrent Type Networks

3. Multi-Label Image Classification Using Convolutional Neural Networks

4. Sentiment Analysis Using Word2Vec and LSTM Network

5. Transfer Learning for Image Classification

6. Real-Time Object Detection using YOLO, JavaCV, and DL4J

7. Stock Price Prediction Using LSTM Network

8. Distributed Deep Learning – Video Classification Using Convolutional LSTM Networks

9. Playing GridWorld Game Using Deep Reinforcement Learning

10. Developing Movie Recommendation Systems Using Factorization Machines

11. Discussion, Current Trends, and Outlook

12. Other Books You May Enjoy

Leave a review - let other readers know what you think

Answers to questions

Answer to question 1: The answer is yes, but not very comfortably. That means a very deep feedforward network such as deep MLP or DBN can classify them with too many iterations.

However, also to speak frankly, MLP is the weakest deep architecture and is not ideal for very high dimensions like this. Moreover, DL4J has deprecated DBN since the DL4J 1.0.0-alpha release. Finally, I would still like to show an MLP network config just in case you want to try it:

// Create network configuration and conduct network training
MultiLayerConfiguration MLPconf = new NeuralNetConfiguration.Builder().seed(seed)
                .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
                .updater(new Adam(0.001)).weightInit(WeightInit.XAVIER).list()
                .layer(0,new DenseLayer.Builder().nIn(numInputs).nOut(32)
                        .weightInit...

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