This is the code repository for Java Deep Learning Projects, published by Packt.
Implement 10 real-world deep learning applications using Deeplearning4j and open source APIs
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.
This book covers the following exciting features:
- Master deep learning and neural network architectures
- Build real-life applications covering image classification, object detection, online trading, transfer learning, and multimedia analytics using DL4J and open-source APIs
- Train ML agents to learn from data using deep reinforcement learning
- Use factorization machines for advanced movie recommendations
- Train DL models on distributed GPUs for faster deep learning with Spark and DL4J
If you feel this book is for you, get your copy today!
All of the code is organized into folders. For example, Chapter02.
The code will look like the following:
<properties>
<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
<java.version>1.8</java.version>
</properties>
Following is what you need for this book: If you are a data scientist, machine learning professional, or deep learning practitioner keen to expand your knowledge by delving into the practical aspects of deep learning with Java, then this book is what you need! Get ready to build advanced deep learning models to carry out complex numerical computations. Some basic understanding of machine learning concepts and a working knowledge of Java are required.
With the following software and hardware list you can run all code files present in the book (Chapter 1-10).
| Chapter | Software required | OS required | Hardware required |
|---|---|---|---|
| 1 | Java/JDK version: 1.8, Spark version: 2.3.0 | Windows 7/10, Linux distro (preferably Ubuntu >14.04), MacOS. | (At least) Core i3 processor, 50GB disk space and 8GB RAM. |
| 2-9 | RJava/JDK version: 1.8, Spark version: 2.3.0, Spark csv_2.11 version: 1.3.0, ND4j backend version: - If GPU configured: nd4j-cuda-9.0-platform - Otherwise: nd4j-native, ND4j version: 1.0.0-alpha, DL4j version: 1.0.0-alpha, Datavec version: 1.0.0-alpha, Arbiter version: 1.0.0-alpha, Logback version: 1.2.3, JavaCV platform version: 1.4.1, HTTP Client version: 4.3.5, Jfreechart:1.0.13, Jcodec:0.2. | Windows 7/10, Linux distro (preferably Ubuntu >14.04), MacOS. | >=Core i5 processor, >=100GB disk space and >=16GB RAM. In addition, Nvidia GPU driver has to be installed with CUDA and CuDNN configured if you want to perform the training on GPU. |
| 10 | Java/JDK version: 1.8, Spark version: 2.3.0, Spark csv_2.11 version: 1.3.0, Jfreechart:1.0.13, RankSys:0.4.3 | (At least) Core i3 processor, 50GB disk space and 8GB RAM. | Windows 7/10, Linux distro (preferably Ubuntu >14.04), MacOS. |
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.
Md. Rezaul Karim 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.
- Predictive Analytics with TensorFlow
- Deep Learning with TensorFlow - Second Edition
- Scala and Spark for Big Data Analytics
- Scala Machine Learning Projects
- Practical Convolutional Neural Networks
- Large Scale Machine Learning with Spark
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