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Java Deep Learning Projects

You're reading from   Java Deep Learning Projects Implement 10 real-world deep learning applications using Deeplearning4j and open source APIs

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Product type Paperback
Published in Jun 2018
Publisher Packt
ISBN-13 9781788997454
Length 436 pages
Edition 1st Edition
Languages
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Author (1):
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Md. Rezaul Karim Md. Rezaul Karim
Author Profile Icon Md. Rezaul Karim
Md. Rezaul Karim
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Table of Contents (13) Chapters Close

Preface 1. Getting Started with Deep Learning 2. Cancer Types Prediction Using Recurrent Type Networks FREE CHAPTER 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)

Although we have been able to solve this multi-label classification problem, the accuracy we experienced was below par. Therefore, in this section, we will see some frequently asked questions (FAQs) that might already be on your mind. Knowing the answers to these questions might help you to improve the accuracy of the CNNs we trained. Answers to these questions can be found in the Appendix:

  1. What are the hyperparameters that I can try tuning while implementing this project?
  2. My machine is getting OOP while running this project. What should I do?
  3. While training the networks with full images, my GPU is getting OOP. What should I do?
  4. I understand that the predictive accuracy using CNN in this project is still very low. Did our network under or overfit? Is there any way to observe how the training went?
  5. I am very interested in implementing the same...
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