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Vergelijkbare producten zoals Learning Apache Mahout Classification
HIGHLIGHT The first and only book on Apache Mahout, an open source tool for leveraging machine learning techniques in large-scale;
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Explore clustering algorithms used with Apache Mahout About This Book * Use Mahout for clustering datasets and gain useful insights;
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Spark, and Mahout About This Book * Implement outstanding Machine Learning use cases on your own analytics models and processes;
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, a committer for the Apache Drill and Apache Mahout projects, is a solutions consultant and well-known speaker and author, currently writing;
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set that allows location constraints to be specified during a search. You'll also develop a text classifier using Lucene and Apache Mahout, a;
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Mahout, a popular machine learning framework. After a detailed review of performance bench-marking and common issues associated with it, yoĆ¼ll;
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learning. Quickly dive into Spark capabilities such as distributed datasets, in-memory caching, and the interactive shell Leverage Spark's;
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will start by learning how to apply machine learning methods to a variety of common tasks including classification, prediction, forecasting;
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Speed up the design and implementation of deep learning solutions using Apache Spark Key Features Explore the world of distributed deep;
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Build efficient data flow and machine learning programs with this flexible, multi-functional open-source cluster-computing framework Key;
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learning, including those that are solving technology requirements, evaluation of methodology advances and algorithm demonstrations. The intent;
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Create scalable machine learning applications to power a modern data-driven business using Spark 2.x About This Book * Get to the;
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how to assess when Apache Thrift is the best tool to be used. To start with, you will run a simple example application, learning the;
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Take a journey toward discovering, learning, and using Apache Spark 3.0. In this book, you will gain expertise on the powerful and;
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Spark * Covers various parts of machine learning including supervised/unsupervised learning & recommendation engines Who This Book Is For;
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developing Microservices in fault-tolerant fashion. This book addressed anyone interested in learning in-memory computing and distributed database;
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, DataFrame based ML Pipelines and SparkR. * Integrations with frameworks such as HDFS, YARN and tools such as Jupyter, Zeppelin, NiFi, Mahout, HBase;
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Develop a range of cutting-edge machine learning projects with Apache Spark using this actionable guide About This Book * Customize;
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Spark * Implement machine learning algorithms such as classification or forecasting In Detail Beyond buzzwords like Big Data or Data;
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learn-to-do-by-yourself approach to learning - learn the concepts, practice the code snippets in Scala, and complete the assignments given to;
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machine learning and analytics applications with Cloud technologies. Beginning Apache Spark 2 gives you an introduction to Apache Spark and shows;
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begin by learning how cloud infrastructure makes it possible to scale your code to large amounts of processing units, without having to pay for;
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insight on variations of the classification process. It discusses ensembles, rare-class learning, distance function learning, active learning;
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concludes with insight on variations of the classification process. It discusses ensembles, rare-class learning, distance function learning, active;
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