Java Deep Learning Projects: Implement 10 real-world deep learning applications using Deeplearning4j and open source APIs

by Md. Rezaul Karim

Overview

Java is one of the most widely used programming languages. With the rise of deep learning, it has become a popular choice of tool among data scientists and machine learning experts.
Java Deep Learning Projects starts with an overview of deep learning concepts and then delves into advanced projects. You will see how to build several projects using different deep neural network architectures such as multilayer perceptrons, Deep Belief Networks, CNN, LSTM, and Factorization Machines.
You will get acquainted with popular deep and machine learning libraries for Java such as Deeplearning4j, Spark ML, and RankSys and you’ll be able to use their features to build and deploy projects on distributed computing environments.
You will then explore advanced domains such as transfer learning and deep reinforcement learning using the Java ecosystem, covering various real-world domains such as healthcare, NLP, image classification, and multimedia analytics with an easy-to-follow approach. Expert reviews and tips will follow every project to give you insights and hacks.
By the end of this book, you will have stepped up your expertise when it comes to deep learning in Java, taking it beyond theory and be able to build your own advanced deep learning systems.

Product Details

ISBN-13:	9781788996525
Publisher:	Packt Publishing
Publication date:	06/29/2018
Sold by:	Barnes & Noble
Format:	eBook
Pages:	436
File size:	29 MB
Note:	This product may take a few minutes to download.

About the Author

Md. Rezaul Karim is a Research Scientist at Fraunhofer FIT, Germany. He is also a PhD candidate at RWTH Aachen University, Germany. Before joining FIT, he was a Researcher at Insight Centre for Data Analytics, Ireland. Before that, he was a Lead Engineer at Samsung Electronics, Korea.
He has 9 years of R&D experience in Java, Scala, Python, and R. He has hands-on experience in Spark, Zeppelin, Hadoop, Keras, scikit-learn, TensorFlow, Deeplearning4j, and H2O. He has published several research papers in top-ranked journals/conferences focusing on bioinformatics and deep learning.

Table of Contents

Table of Contents

1. Getting Started with Deep Learning
2. Cancer Type Prediction using Recurrent Type Networks
3. Image Classification using Convolutional Neural Networks
4. Sentiment Analysis using Word2Vec and LSTM Networks
5. Image Classification using Transfer Learning
6. Real-Time Object Detection Using YOLO, JavaCV, and DL4J
7. Stock Price Prediction Using the LSTM Network
8. Distributed Deep Learning – Video Classification Using Convolutional-LSTM Networks
9. Using Deep Reinforcement Learning for a GridWorld Game
10. Movie Recommendation System using Factorization Machines
11. Discussion, Current Trends, and Outlook