Hadoop Mahout实战指南：探索LDA与机器学习算法

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"《Mahout in Action》是一本专为IT专业人士编写的实用指南，它聚焦于在Apache Hadoop平台上实施机器学习的实战技术。本书由业内知名的作者们，如Sean Owen、Robin Anil、Ted Dunning和Ellen Friedman共同编著，汇集了他们在构建基于Hadoop的开源机器学习系统方面的丰富经验。书中详细介绍了多种算法，包括但不限于主题模型（如Latent Dirichlet Allocation, LDA），这些算法被广泛应用于分类和推荐系统。这本书特别强调实用性，适合那些希望在大数据环境下应用机器学习技术的读者。由于内容包含音频和视频链接，读者需要Adobe Acrobat Reader来访问这些媒体资源。本书的版权归Manning Publications所有，未经许可，禁止任何形式的复制或传输，除非获得出版商的书面授权。《Mahout in Action》旨在通过实例驱动的方式，帮助读者掌握如何在实际项目中集成和优化这些算法，无论是用于文本分析、用户行为预测还是其他商业场景。书中不仅涵盖了理论知识，还提供了清晰的代码示例，便于读者理解和实践。此外，对于批量购买或获取更多信息，读者可以通过Manning Publications的官方网站或直接联系他们的销售部门获取折扣和详细资讯。《Mahout in Action》是一本不可或缺的工具书，对于那些希望通过Hadoop进行高级机器学习实践的工程师和数据科学家来说，它不仅提供了一个深入理解 Mahout 框架的窗口，而且也是一份实操指南，助力他们在这个快速发展的领域中提升技能和效率。"

CONTENTS xv

16.3 Building a training pipeline for large systems 313

Acquiring and retaining large-scale data 314

■

Denormalizing

and downsampling 316

■

Training pitfalls 318

■

Reading

and encoding data at speed 320

16.4 Integrating a Mahout classifier 324

Plan ahead: key issues for integration 325

■

Model

serialization 330

16.5 Example: a Thrift-based classification server 332

Running the classification server 336

■

Accessing the

classifier service 338

16.6 Summary 340

Case study: Shop It To Me 341

17.1 Why Shop It To Me chose Mahout 342

What Shop It To Me does 342

■

Why Shop It To Me needed a

classification system 342

■

Mahout outscales the rest 343

17.2 General structure of the email marketing system 344

17.3 Training the model 346

Defining the goal of the classification project 346

■

Partitioning by

time 348

■

Avoiding target leaks 348

■

Learning algorithm

tweaks 348

■

Feature vector encoding 349

17.4 Speeding up classification 352

Linear combination of feature vectors 353

■

Linear expansion

of model score 354

17.5 Summary 356

appendix A JVM tuning 359

appendix B Mahout math 362

appendix C Resources 367

index 369

Licensed to chunming liu <chunming02@163.com>

PREFACExviii

from the reality of applying the techniques. Mahout in Action aims to be accessible, to

unearth the interesting nuggets of insight for the enthusiast, and to save the practitio-

ner time in getting work done. I hope it provides you more “a-ha!” moments than

“wha…?” moments.

EAN OWEN

My (Robin’s) interest in machine learning started during my days in college, back in

2006. At that time, I was working as an intern with a group of people designing a per-

sonalized recommendation engine. That group flourished and became a company

called Minekey; I was invited to join as one of its core developers. The next four years

of my life were spent implementing and experimenting with machine learning tech-

niques. Somewhere along that path, I stumbled across Mahout and started contribut-

ing as a Google Summer of Code student. The next thing I knew, I was contributing

algorithms and patches to its codebase, tuning and optimizing performance, and

helping other folks on the mailing list.

I am really fortunate to be part of a wonderful and growing community of develop-

ers, researchers, and enthusiasts of machine learning. As more and more companies

are adopting Mahout, it is becoming a mainstream library of machine learning. I

really hope you enjoy reading this book.

OBIN ANIL

I (Ted) came to the application side of projects from research in machine learning.

Formerly an academic, I have subsequently been involved in a number of startups,

and I have applied machine learning to all of these practical application settings.

Previously, I (Ellen) worked in research laboratories in biochemistry and molecu-

lar biology. In addition to having lots of experience with data, I’ve written extensively

on technical subjects. Throughout it all, I’ve remained fascinated by data and how it

speaks to us. I have tried to bring this insight to Mahout in Action.

Both of us see that open source only works with input from an active and broad

community of participants. A major part of Mahout’s success comes from those who

have used the software and brought their experience back to the project via discus-

sions in mailing lists, bug fixes, and suggestions.

For this reason, Mahout in Action not only provides useful explanations of code, but

also guidance regarding the concepts behind the code. This introduction to the

framework behind the code will enable you to effectively join in and benefit from the

interactive Mahout discussion. We hope this book not only helps the readers of this

book, but also helps to expand and enrich Mahout itself.

ED DUNNING AND ELLEN FRIEDMAN

Licensed to chunming liu <chunming02@163.com>

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Hadoop Mahout实战指南：探索LDA与机器学习算法

Mahout In Action英文完整版

mahout in action中的源码

Mahout in Action 2012

Mahout in action 中文版

Mahout in action清晰完整版

Mahout In Action 2012 Source Code

opencv_python-4.1.0.25-cp37-cp37m-linux_armv7l.whl

onnxruntime-1.13.1-cp38-cp38-win_amd64.whl

元学习，小样本图像数据集：FC100数据集

numpy-1.19.5-cp39-cp39-linux_armv7l.whl

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