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


Build and deploy powerful neural network models using the latest Java deep learning libraries

Key Features

  • Understand DL with Java by implementing real-world projects
  • Master implementations of various ANN models and build your own DL systems
  • Develop applications using NLP, image classification, RL, and GPU processing

Book Description

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.

What you will learn

  • 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
  • Ease your learning experience through 69 FAQs

Who This Book Is For

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.

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
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