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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
Toc

Table of Contents (13) Chapters close

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

Frequently asked questions (FAQs)

Now that we have solved the Titanic survival prediction problem with an acceptable level of accuracy, there are other practical aspects of this problem and overall deep learning phenomena that need to be considered too. In this section, we will see some frequently asked questions that might be already in your mind. Answers to these questions can be found in Appendix A.

  1. Can't we use MLP to solve the cancer type prediction by handling this too high-dimensional data?
  2. Which activation and loss function can be used with RNN type nets?
  3. What is the best way of recurrent net weight initialization?
  4. Which updater and optimization algorithm should be used?
  5. In the Titanic survival prediction problem, we did not experience good accuracy. What could be possible reasons and how can we improve the accuracy?
  6. The predictive accuracy for cancer type prediction...
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