Java Deep Learning Cookbook : Train neural networks for classification, NLP, and reinforcement learning using Deeplearning4j (Paperback)
暫譯: Java 深度學習食譜：使用 Deeplearning4j 訓練神經網絡進行分類、自然語言處理和強化學習 (平裝本)

Raj, Rahul

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

買這商品的人也買了...

相關主題

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

- 安裝和配置 Deeplearning4j，以從零開始實現深度學習模型
- 探索在 Java 中開發、訓練和微調神經網絡模型的食譜
- 使用包含圖像、文本和時間序列數據的數據集來建模神經網絡

#### 書籍描述

Java 是世界上最廣泛使用的程式語言之一。通過本書，您將學習如何使用 Deeplearning4j (DL4J) 進行深度學習，這是最受歡迎的 Java 庫，用於高效訓練神經網絡。

本書首先將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本概念，並利用您的知識從零開始創建一個用於二元分類的深度神經網絡。隨著學習的深入，您將發現如何在 DL4J 中構建卷積神經網絡 (CNN)，並理解如何從文本構建數值向量。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，您將學習如何從 Keras 導入模型並更改預訓練 DL4J 模型中的配置。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。

在本書結束時，您將清楚了解如何使用 DL4J 在 Java 中構建穩健的深度學習應用程序。

---

#### 您將學到的內容

- 使用 DL4J 執行數據標準化和處理
- 使用 DL4J 構建深度神經網絡
- 實現 CNN 以解決圖像分類問題
- 訓練自編碼器以使用 DL4J 解決異常檢測問題
- 執行基準測試和優化以改善模型性能
- 使用 RL4J 實現強化學習以應對現實世界的用例
- 在分佈式系統中利用 DL4J 的能力

#### 本書適合誰

如果您是數據科學家、機器學習開發人員或希望在 Java 中實現深度學習模型的深度學習愛好者，那麼本書適合您。需要具備基本的 Java 程式設計知識以及一些機器學習和神經網絡的經驗，以便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 擁有超過 7 年的 IT 行業經驗，專注於軟體開發、業務分析、客戶溝通以及中大型專案的諮詢，涵蓋多個領域。目前，他在一家頂尖的軟體開發公司擔任首席軟體工程師。他在開發活動方面擁有豐富的經驗，包括需求分析、設計、編碼、實施、程式碼審查、測試、用戶培訓和增強功能。他撰寫了多篇關於 Java 中神經網絡的文章，並在 DL4J/官方 Java 社群頻道上發表。他也是一名經過 Vskills 認證的機器學習專業人士，Vskills 是印度最大的政府認證機構。

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)


  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

類似商品