Search icon CANCEL
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
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

Arrow left icon
Product type Paperback
Published in Jun 2018
Publisher Packt
ISBN-13 9781788997454
Length 436 pages
Edition 1st Edition
Languages
Arrow right icon
Author (1):
Arrow left icon
Md. Rezaul Karim Md. Rezaul Karim
Author Profile Icon Md. Rezaul Karim
Md. Rezaul Karim
Arrow right icon
View More author details
Toc

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)

We have analyzed the completed projects and looked at recent trends. Based on these, there might be several questions in your mind. In this section, I will try to devise some such questions and provide sample answers:

  1. In this chapter, we argued that using GAN, we could solve many research problems. Is there any GAN implementation in DL4J?
  2. In this chapter, we argued that using CapsNet is a better idea for handling images having different shapes and orientation. Is there any implementation for CapsNet in DL4J?
  3. In Chapter 1, Getting Started with Deep Learning, we discussed DBNs and restricted Boltzmann machines as their basic building blocks. However, we have not used DBNs in any of the completed projects. What is the reason for this?
  4. In this chapter, we argued that using unsupervised anomaly detection from IoT sensor data or images is an emerging...
lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime