Java Deep Learning Cookbook : Train neural networks for classification, NLP, and reinforcement learning using Deeplearning4j (Paperback)

Raj, Rahul

Java Deep Learning Cookbook : Train neural networks for classification, NLP, and reinforcement learning using Deeplearning4j (Paperback)

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商品描述

Key Features

  • Install and configure Deeplearning4j to implement deep learning models from scratch
  • Explore recipes for developing, training, and fine-tuning your neural network models in Java
  • Model neural networks using datasets containing images, text, and time-series data

Book Description

Java is one of the most widely used programming languages in the world. With this book, you will see how to perform deep learning using Deeplearning4j (DL4J) – the most popular Java library for training neural networks efficiently.

This book starts by showing you how to install and configure Java and DL4J on your system. You will then gain insights into deep learning basics and use your knowledge to create a deep neural network for binary classification from scratch. As you progress, you will discover how to build a convolutional neural network (CNN) in DL4J, and understand how to construct numeric vectors from text. This deep learning book will also guide you through performing anomaly detection on unsupervised data and help you set up neural networks in distributed systems effectively. In addition to this, you will learn how to import models from Keras and change the configuration in a pre-trained DL4J model. Finally, you will explore benchmarking in DL4J and optimize neural networks for optimal results.

By the end of this book, you will have a clear understanding of how you can use DL4J to build robust deep learning applications in Java.

What you will learn

  • Perform data normalization and wrangling using DL4J
  • Build deep neural networks using DL4J
  • Implement CNNs to solve image classification problems
  • Train autoencoders to solve anomaly detection problems using DL4J
  • Perform benchmarking and optimization to improve your model's performance
  • Implement reinforcement learning for real-world use cases using RL4J
  • Leverage the capabilities of DL4J in distributed systems

Who this book is for

If you are a data scientist, machine learning developer, or a deep learning enthusiast who wants to implement deep learning models in Java, this book is for you. Basic understanding of Java programming as well as some experience with machine learning and neural networks is required to get the most out of this book.

商品描述(中文翻譯)

主要特點


  • 安裝並配置Deeplearning4j以從頭開始實現深度學習模型

  • 探索使用Java開發、訓練和微調神經網絡模型的方法

  • 使用包含圖像、文本和時間序列數據的數據集建模神經網絡

書籍描述

Java是世界上使用最廣泛的編程語言之一。通過本書，您將了解如何使用Deeplearning4j（DL4J）進行深度學習，這是最受歡迎的Java庫，可高效地訓練神經網絡。

本書首先向您展示如何在系統上安裝和配置Java和DL4J。然後，您將深入了解深度學習的基礎知識，並利用這些知識從頭開始創建用於二元分類的深度神經網絡。隨著學習的進展，您將發現如何在DL4J中構建卷積神經網絡（CNN），並了解如何從文本中構建數值向量。本書還將指導您在無監督數據上執行異常檢測，並幫助您有效地在分佈式系統中設置神經網絡。此外，您還將學習如何從Keras導入模型並更改預訓練的DL4J模型的配置。最後，您將探索DL4J中的基準測試並優化神經網絡以獲得最佳結果。

通過閱讀本書，您將清楚了解如何使用DL4J在Java中構建強大的深度學習應用程序。


您將學到什麼


  • 使用DL4J進行數據歸一化和數據整理

  • 使用DL4J構建深度神經網絡

  • 實現CNN解決圖像分類問題

  • 使用DL4J訓練自編碼器解決異常檢測問題

  • 進行基準測試和優化以提高模型性能

  • 使用RL4J實現強化學習解決實際應用案例

  • 在分佈式系統中利用DL4J的能力

適合閱讀對象

如果您是數據科學家、機器學習開發人員或深度學習愛好者，並且希望在Java中實現深度學習模型，那麼本書適合您。您需要具備基本的Java編程理解以及一些機器學習和神經網絡的經驗，以充分利用本書的內容。

作者簡介

Rahul Raj has more than 7 years of IT industry experience in software development, business analysis, client communication, and consulting on medium-/large-scale projects in multiple domains. Currently, he works as a lead software engineer in a top software development firm. He has extensive experience in development activities comprising requirement analysis, design, coding, implementation, code review, testing, user training, and enhancements. He has written a number of articles about neural networks in Java and they are featured by DL4J/ official Java community channels. He is also a certified machine learning professional, certified by Vskills, the largest government certification body in India.

作者簡介(中文翻譯)

Rahul Raj在IT行業擁有超過7年的軟體開發、業務分析、客戶溝通和咨詢的經驗，並參與了多個領域的中大型項目。目前，他在一家頂尖軟體開發公司擔任首席軟體工程師。他在開發活動中擁有豐富的經驗，包括需求分析、設計、編碼、實施、代碼審查、測試、用戶培訓和增強。他撰寫了多篇有關Java神經網絡的文章，並在DL4J/官方Java社區頻道上亮相。他還是一位由印度最大的政府認證機構Vskills認證的機器學習專業人士。

目錄大綱

  1. Introduction to Deep Learning in Java
  2. Data Extraction, Transform and Loading
  3. Building Deep Neural Networks for Binary classification
  4. Building Convolutional Neural Networks
  5. Implementing NLP
  6. Constructing LTSM Network for time series
  7. Constructing LTSM Neural network for sequence classification
  8. Performing Anomaly detection on unsupervised data
  9. Using RL4J for Reinforcement learning
  10. Developing applications in distributed environment
  11. Applying Transfer Learning to network models
  12. Benchmarking and Neural Network Optimization

目錄大綱(中文翻譯)

- 深度學習在Java中的介紹
- 數據提取、轉換和加載
- 構建用於二元分類的深度神經網絡
- 構建卷積神經網絡
- 實現自然語言處理
- 構建用於時間序列的LTSM網絡
- 構建用於序列分類的LTSM神經網絡
- 對無監督數據進行異常檢測
- 使用RL4J進行強化學習
- 在分佈式環境中開發應用程序
- 將轉移學習應用於網絡模型
- 基準測試和神經網絡優化

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