Author: Sean Owen
Publisher: Simon and Schuster
ISBN: 1638355371
Category : Computers
Languages : en
Pages : 616
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Book Description
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. Owners of a Manning pBook purchased anywhere in the world can download a free eBook from manning.com at any time. They can do so multiple times and in any or all formats available (PDF, ePub or Kindle). To do so, customers must register their printed copy on Manning's site by creating a user account and then following instructions printed on the pBook registration insert at the front of the book. 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 Meet Apache Mahout PART 1 RECOMMENDATIONS Introducing recommenders Representing recommender data Making recommendations Taking recommenders to production Distributing recommendation computations PART 2 CLUSTERING Introduction to clustering Representing data Clustering algorithms in Mahout Evaluating and improving clustering quality Taking clustering to production Real-world applications of clustering PART 3 CLASSIFICATION Introduction to classification Training a classifier Evaluating and tuning a classifier Deploying a classifier Case study: Shop It To Me
Author: Uma N. Dulhare
Publisher: John Wiley & Sons
ISBN: 1119654742
Category : Computers
Languages : en
Pages : 544
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Book Description
This book is intended for academic and industrial developers, exploring and developing applications in the area of big data and machine learning, including those that are solving technology requirements, evaluation of methodology advances and algorithm demonstrations. The intent of this book is to provide awareness of algorithms used for machine learning and big data in the academic and professional community. The 17 chapters are divided into 5 sections: Theoretical Fundamentals; Big Data and Pattern Recognition; Machine Learning: Algorithms & Applications; Machine Learning's Next Frontier and Hands-On and Case Study. While it dwells on the foundations of machine learning and big data as a part of analytics, it also focuses on contemporary topics for research and development. In this regard, the book covers machine learning algorithms and their modern applications in developing automated systems. Subjects covered in detail include: Mathematical foundations of machine learning with various examples. An empirical study of supervised learning algorithms like Naïve Bayes, KNN and semi-supervised learning algorithms viz. S3VM, Graph-Based, Multiview. Precise study on unsupervised learning algorithms like GMM, K-mean clustering, Dritchlet process mixture model, X-means and Reinforcement learning algorithm with Q learning, R learning, TD learning, SARSA Learning, and so forth. Hands-on machine leaning open source tools viz. Apache Mahout, H2O. Case studies for readers to analyze the prescribed cases and present their solutions or interpretations with intrusion detection in MANETS using machine learning. Showcase on novel user-cases: Implications of Electronic Governance as well as Pragmatic Study of BD/ML technologies for agriculture, healthcare, social media, industry, banking, insurance and so on.
Author: Timothy Potter
Publisher: Simon and Schuster
ISBN: 1638351236
Category : Computers
Languages : en
Pages : 939
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Book Description
Summary Solr in Action is a comprehensive guide to implementing scalable search using Apache Solr. This clearly written book walks you through well-documented examples ranging from basic keyword searching to scaling a system for billions of documents and queries. It will give you a deep understanding of how to implement core Solr capabilities. About the Book Whether you're handling big (or small) data, managing documents, or building a website, it is important to be able to quickly search through your content and discover meaning in it. Apache Solr is your tool: a ready-to-deploy, Lucene-based, open source, full-text search engine. Solr can scale across many servers to enable real-time queries and data analytics across billions of documents. Solr in Action teaches you to implement scalable search using Apache Solr. This easy-to-read guide balances conceptual discussions with practical examples to show you how to implement all of Solr's core capabilities. You'll master topics like text analysis, faceted search, hit highlighting, result grouping, query suggestions, multilingual search, advanced geospatial and data operations, and relevancy tuning. This book assumes basic knowledge of Java and standard database technology. No prior knowledge of Solr or Lucene is required. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. What's Inside How to scale Solr for big data Rich real-world examples Solr as a NoSQL data store Advanced multilingual, data, and relevancy tricks Coverage of versions through Solr 4.7 About the Authors Trey Grainger is a director of engineering at CareerBuilder. Timothy Potter is a senior member of the engineering team at LucidWorks. The authors work on the scalability and reliability of Solr, as well as on recommendation engine and big data analytics technologies. Table of Contents PART 1 MEET SOLR Introduction to Solr Getting to know Solr Key Solr concepts Configuring Solr Indexing Text analysis PART 2 CORE SOLR CAPABILITIES Performing queries and handling results Faceted search Hit highlighting Query suggestions Result grouping/field collapsing Taking Solr to production PART 3 TAKING SOLR TO THE NEXT LEVEL SolrCloud Multilingual search Complex query operations Mastering relevancy
Author: Grant Ingersoll
Publisher: Simon and Schuster
ISBN: 1638353867
Category : Computers
Languages : en
Pages : 467
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Book Description
Summary Taming Text, winner of the 2013 Jolt Awards for Productivity, is a hands-on, example-driven guide to working with unstructured text in the context of real-world applications. This book explores how to automatically organize text using approaches such as full-text search, proper name recognition, clustering, tagging, information extraction, and summarization. The book guides you through examples illustrating each of these topics, as well as the foundations upon which they are built. About this Book There is so much text in our lives, we are practically drowningin it. Fortunately, there are innovative tools and techniquesfor managing unstructured information that can throw thesmart developer a much-needed lifeline. You'll find them in thisbook. Taming Text is a practical, example-driven guide to working withtext in real applications. This book introduces you to useful techniques like full-text search, proper name recognition,clustering, tagging, information extraction, and summarization.You'll explore real use cases as you systematically absorb thefoundations upon which they are built.Written in a clear and concise style, this book avoids jargon, explainingthe subject in terms you can understand without a backgroundin statistics or natural language processing. Examples arein Java, but the concepts can be applied in any language. Written for Java developers, the book requires no prior knowledge of GWT. Purchase of the print book comes with an offer of a free PDF, ePub, and Kindle eBook from Manning. Also available is all code from the book. Winner of 2013 Jolt Awards: The Best Books—one of five notable books every serious programmer should read. What's Inside When to use text-taming techniques Important open-source libraries like Solr and Mahout How to build text-processing applications About the Authors Grant Ingersoll is an engineer, speaker, and trainer, a Lucenecommitter, and a cofounder of the Mahout machine-learning project. Thomas Morton is the primary developer of OpenNLP and Maximum Entropy. Drew Farris is a technology consultant, software developer, and contributor to Mahout,Lucene, and Solr. "Takes the mystery out of verycomplex processes."—From the Foreword by Liz Liddy, Dean, iSchool, Syracuse University Table of Contents Getting started taming text Foundations of taming text Searching Fuzzy string matching Identifying people, places, and things Clustering text Classification, categorization, and tagging Building an example question answering system Untamed text: exploring the next frontier
Author: Jukka L. Zitting
Publisher: Simon and Schuster
ISBN: 1638352631
Category : Computers
Languages : en
Pages : 365
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Book Description
Summary Tika in Action is a hands-on guide to content mining with Apache Tika. The book's many examples and case studies offer real-world experience from domains ranging from search engines to digital asset management and scientific data processing. About the Technology Tika is an Apache toolkit that has built into it everything you and your app need to know about file formats. Using Tika, your applications can discover and extract content from digital documents in almost any format, including exotic ones. About this Book Tika in Action is the ultimate guide to content mining using Apache Tika. You'll learn how to pull usable information from otherwise inaccessible sources, including internet media and file archives. This example-rich book teaches you to build and extend applications based on real-world experience with search engines, digital asset management, and scientific data processing. In addition to architectural overviews, you'll find detailed chapters on features like metadata extraction, automatic language detection, and custom parser development. This book is written for developers who are new to both Scala and Lift and covers just enough Scala to get you started. Purchase of the print book comes with an offer of a free PDF, ePub, and Kindle eBook from Manning. Also available is all code from the book. What's Inside Crack MS Word, PDF, HTML, and ZIP Integrate with search engines, CMS, and other data sources Learn through experimentation Many examples This book requires no previous knowledge of Tika or text mining techniques. It assumes a working knowledge of Java. ========================================== Table of Contents PART 1 GETTING STARTED The case for the digital Babel fish Getting started with Tika The information landscape PART 2 TIKA IN DETAIL Document type detection Content extraction Understanding metadata Language detection What's in a file? PART 3 INTEGRATION AND ADVANCED USE The big picture Tika and the Lucene search stack Extending Tika PART 4 CASE STUDIES Powering NASA science data systems Content management with Apache Jackrabbit Curating cancer research data with Tika The classic search engine example
Author: Otis Gospodnetic
Publisher: Simon and Schuster
ISBN: 1638354731
Category : Computers
Languages : en
Pages : 742
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Book Description
When Lucene first hit the scene five years ago, it was nothing short ofamazing. By using this open-source, highly scalable, super-fast search engine,developers could integrate search into applications quickly and efficiently.A lot has changed since then-search has grown from a "nice-to-have" featureinto an indispensable part of most enterprise applications. Lucene now powerssearch in diverse companies including Akamai, Netflix, LinkedIn,Technorati, HotJobs, Epiphany, FedEx, Mayo Clinic, MIT, New ScientistMagazine, and many others. Some things remain the same, though. Lucene still delivers high-performancesearch features in a disarmingly easy-to-use API. Due to its vibrant and diverseopen-source community of developers and users, Lucene is relentlessly improving,with evolutions to APIs, significant new features such as payloads, and ahuge increase (as much as 8x) in indexing speed with Lucene 2.3. And with clear writing, reusable examples, and unmatched advice on bestpractices, Lucene in Action, Second Edition is still the definitive guide todeveloping with Lucene. Purchase of the print book comes with an offer of a free PDF, ePub, and Kindle eBook from Manning. Also available is all code from the book.
Author: Ashish Gupta
Publisher: Packt Publishing Ltd
ISBN: 1783284447
Category : Computers
Languages : en
Pages : 131
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Book Description
Explore clustering algorithms used with Apache Mahout About This Book Use Mahout for clustering datasets and gain useful insights Explore the different clustering algorithms used in day-to-day work A practical guide to create and evaluate your own clustering models using real world data sets Who This Book Is For This book is for developers who want to try out clustering on large datasets using Mahout. It will also be useful for those users who don't have background in Mahout, but have knowledge of basic programming and are familiar with basics of machine learning and clustering. It will be helpful if you know about clustering techniques with some other tool. What You Will Learn Explore clustering algorithms and cluster evaluation techniques Learn different types of clustering and distance measuring techniques Perform clustering on your data using K-Means clustering Discover how canopy clustering is used as pre-process step for K-Means Use the Fuzzy K-Means algorithm in Apache Mahout Implement Streaming K-Means clustering in Mahout Learn Spectral K-Means clustering implementation of Mahout In Detail As more and more organizations are discovering the use of big data analytics, interest in platforms that provide storage, computation, and analytic capabilities has increased. Apache Mahout caters to this need and paves the way for the implementation of complex algorithms in the field of machine learning to better analyse your data and get useful insights into it. Starting with the introduction of clustering algorithms, this book provides an insight into Apache Mahout and different algorithms it uses for clustering data. It provides a general introduction of the algorithms, such as K-Means, Fuzzy K-Means, StreamingKMeans, and how to use Mahout to cluster your data using a particular algorithm. You will study the different types of clustering and learn how to use Apache Mahout with real world data sets to implement and evaluate your clusters. This book will discuss about cluster improvement and visualization using Mahout APIs and also explore model-based clustering and topic modelling using Dirichlet process. Finally, you will learn how to build and deploy a model for production use. Style and approach This book is a hand's-on guide with examples using real-world datasets. Each chapter begins by explaining the algorithm in detail and follows up with showing how to use mahout for that algorithm using example data-sets.
Author: Dan Zhang
Publisher: CRC Press
ISBN: 1498736882
Category : Computers
Languages : en
Pages : 435
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Book Description
This book discusses advanced topics such as R core programing, object oriented R programing, parallel computing with R, and spatial data types. The author leads readers to merge mature and effective methdologies in traditional programing to R programing. It shows how to interface R with C, Java, and other popular programing laguages and platforms.
Author: Ted Dunning
Publisher: "O'Reilly Media, Inc."
ISBN: 1491915714
Category : Computers
Languages : en
Pages : 59
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Book Description
Building a simple but powerful recommendation system is much easier than you think. Approachable for all levels of expertise, this report explains innovations that make machine learning practical for business production settings—and demonstrates how even a small-scale development team can design an effective large-scale recommendation system. Apache Mahout committers Ted Dunning and Ellen Friedman walk you through a design that relies on careful simplification. You’ll learn how to collect the right data, analyze it with an algorithm from the Mahout library, and then easily deploy the recommender using search technology, such as Apache Solr or Elasticsearch. Powerful and effective, this efficient combination does learning offline and delivers rapid response recommendations in real time. Understand the tradeoffs between simple and complex recommenders Collect user data that tracks user actions—rather than their ratings Predict what a user wants based on behavior by others, using Mahoutfor co-occurrence analysis Use search technology to offer recommendations in real time, complete with item metadata Watch the recommender in action with a music service example Improve your recommender with dithering, multimodal recommendation, and other techniques
Author: Roland Smith
Publisher: Perfection Learning
ISBN: 9781606865156
Category : Burma
Languages : en
Pages : 0
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Book Description
When 14-year-old Nick is sent to his father in Burma to escape the 1941 bombings in London, he finds himself in even more danger as Japanese soldiers arrive and take control of the plantation house.
