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

Product type Paperback

Published in Jun 2018

Publisher Packt

ISBN-13 9781788997454

Length 436 pages

Edition 1st Edition

Languages

Java

Tools

Deeplearning4j

Concepts

Deep Learning

Table of Contents (17) Chapters

Title Page

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Contributors

Preface

1. Getting Started with Deep Learning FREE CHAPTER

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

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Index

Current trends and outlook

As a researcher, I work as a Program Committee (PC) member for conferences such as WWW'2018, ISWC'2018, ESWC'2017/2018, and ESWC SemDeep'2018 international workshops. Apart from these, I am also a guest editor for International Semantic Web Journal, Journal of Cloud Computing, and Briefings in Bioinformatics.

While reviewing numerous papers for these conferences and journals, I found that researchers have not limited themselves to developing emerging use cases and analytical solutions using original RNN, CNN, DBN or autoencoders. They are coming up with ideas across new architectures by combining them for diverse domains.

Current trends

As discussed in Chapter 1, Getting Started with Deep Learning, researchers have recently proposed so many emergent DL architectures. These include not only improving CNN/RNN and their variants but also some other special types of architecture: Deep SpatioTemporal Neural Networks (DST-NNs), Multi-Dimensional Recurrent Neural Networks...