Mahout in Action (Paperback)
暫譯: 《Mahout 實戰》

Sean Owen, Robin Anil, Ted Dunning, Ellen Friedman

Mahout in Action (Paperback)
  • 出版商: Manning
  • 出版日期: 2011-10-17
  • 售價: $1,480
  • 貴賓價: 9.5 折 $1,406
  • 語言: 英文
  • 頁數: 416
  • 裝訂: Paperback
  • ISBN: 1935182684
  • ISBN-13: 9781935182689
  • 相關分類: Machine Learning
  • 相關翻譯: Mahout實戰 (簡中版)

    • 立即出貨(限量) (庫存=2)

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商品描述

Summary

Mahout in Action is a hands-on introduction to machine learning with Apache Mahout. Following real-world examples, the book presents practical use cases and then illustrates how Mahout can be applied to solve them. Includes a free audio- and video-enhanced ebook.

About the Technology

A computer system that learns and adapts as it collects data can be really powerful. Mahout, Apache's open source machine learning project, captures the core algorithms of recommendation systems, classification, and clustering in ready-to-use, scalable libraries. With Mahout, you can immediately apply to your own projects the machine learning techniques that drive Amazon, Netflix, and others.

About this Book

This book covers machine learning using Apache Mahout. Based on experience with real-world applications, it introduces practical use cases and illustrates how Mahout can be applied to solve them. It places particular focus on issues of scalability and how to apply these techniques against large data sets using the Apache Hadoop framework.

This book is written for developers familiar with Java - no prior experience with Mahout is assumed.

What's Inside
  • Use group data to make individual recommendations
  • Find logical clusters within your data
  • Filter and refine with on-the-fly classification
  • Free audio and video extras
Table of Contents
PART 1 RECOMMENDATIONS
PART 2 CLUSTERING
PART 3 CLASSIFICATION
  1. Meet Apache Mahout
  2. Introducing recommenders
  3. Representing recommender data
  4. Making recommendations
  5. Taking recommenders to production
  6. Distributing recommendation computations
  7. Introduction to clustering
  8. Representing data
  9. Clustering algorithms in Mahout
  10. Evaluating and improving clustering quality
  11. Taking clustering to production
  12. Real-world applications of clustering
  13. Introduction to classification
  14. Training a classifier
  15. Evaluating and tuning a classifier
  16. Deploying a classifier
  17. Case study: Shop It To Me

商品描述(中文翻譯)

**摘要**

《Mahout in Action》是一本針對Apache Mahout的機器學習實作入門書籍。透過真實世界的範例，本書呈現實用的使用案例，並說明如何應用Mahout來解決這些問題。書中包含免費的音頻和視頻增強電子書。

**關於技術**

一個隨著數據收集而學習和適應的計算機系統可以非常強大。Mahout是Apache的開源機器學習專案，捕捉了推薦系統、分類和聚類的核心算法，並提供即用的可擴展庫。使用Mahout，您可以立即將驅動Amazon、Netflix等公司的機器學習技術應用到自己的專案中。

**關於本書**

本書涵蓋了使用Apache Mahout的機器學習。基於對真實世界應用的經驗，它介紹了實用的使用案例，並說明如何應用Mahout來解決這些問題。特別關注可擴展性問題，以及如何使用Apache Hadoop框架對大型數據集應用這些技術。

本書是為熟悉Java的開發人員撰寫的，並不假設讀者有Mahout的先前經驗。

**內容概覽**

- 使用群組數據進行個別推薦
- 在數據中尋找邏輯聚類
- 透過即時分類進行過濾和精煉
- 免費的音頻和視頻附加內容

**目錄**

**第一部分 推薦系統**

**第二部分 聚類**

**第三部分 分類**

1. 認識Apache Mahout
2. 介紹推薦系統
3. 表示推薦數據
4. 進行推薦
5. 將推薦系統投入生產
6. 分配推薦計算
7. 聚類簡介
8. 表示數據
9. Mahout中的聚類算法
10. 評估和改善聚類質量
11. 將聚類投入生產
12. 聚類的真實世界應用
13. 分類簡介
14. 訓練分類器
15. 評估和調整分類器
16. 部署分類器
17. 案例研究：Shop It To Me

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