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

使用最新的Java深度學習庫構建和部署強大的神經網絡模型

主要特點:
- 通過實施真實項目來理解Java中的深度學習
- 掌握各種人工神經網絡模型的實現，並構建自己的深度學習系統
- 使用自然語言處理（NLP）、圖像分類、強化學習和GPU處理開發應用程序

書籍描述:
Java是最廣泛使用的編程語言之一。隨著深度學習的興起，它已成為數據科學家和機器學習專家中的熱門工具選擇。

《Java深度學習項目》首先概述了深度學習概念，然後深入介紹了高級項目。您將學習如何使用不同的深度神經網絡架構（如多層感知器、深度信念網絡、卷積神經網絡、長短期記憶網絡和分解機）構建多個項目。

您將熟悉Java中流行的深度學習和機器學習庫，如Deeplearning4j、Spark ML和RankSys，並能夠使用它們的功能在分布式計算環境中構建和部署項目。

然後，您將使用Java生態系統探索轉移學習和深度強化學習等高級領域，涵蓋醫療保健、自然語言處理、圖像分類和多媒體分析等各種實際領域，並以易於理解的方式進行介紹。每個項目都有專家評論和技巧，以提供深入見解和技巧。

通過閱讀本書，您將提升在Java中深度學習的專業知識，將其超越理論，並能夠構建自己的高級深度學習系統。

您將學到:
- 掌握深度學習和神經網絡架構
- 使用DL4J和開源API構建涵蓋圖像分類、物體檢測、在線交易、轉移學習和多媒體分析的實際應用程序
- 使用深度強化學習訓練機器學習代理從數據中學習
- 使用分解機進行高級電影推薦
- 使用Spark和DL4J在分布式GPU上訓練深度學習模型，實現更快的深度學習
- 通過69個常見問題解答來輕鬆學習

本書適合對深度學習有興趣的數據科學家、機器學習專業人士或深度學習從業者，希望通過深入研究Java中深度學習的實際方面來擴展知識。需要具備一些機器學習概念的基本理解和Java的工作知識。

目錄:
1. 深度學習入門
2. 使用循環型網絡進行癌症類型預測
3. 使用卷積神經網絡進行圖像分類
4. 使用Word2Vec和LSTM網絡進行情感分析
5. 使用轉移學習進行圖像分類
6. 使用YOLO、JavaCV和DL4J進行實時物體檢測
7. 使用LSTM網絡進行股票價格預測
8. 分布式深度學習-使用卷積-LSTM網絡進行視頻分類
9. 使用深度強化學習進行GridWorld遊戲
10. 使用分解機進行電影推薦系統
11. 討論、當前趨勢和展望