e-book Scaling up Machine Learning: Parallel and Distributed Approaches

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  1. Scalable data science with R - O'Reilly Media
  2. ISBN 10: 0521192242
  3. Scalability

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Get In-Stock Alert. Delivery not available. Pickup not available. Demand for parallelizing learning algorithms is highly task-specific: in some settings it is driven by the enormous dataset sizes, in others by model complexity or by real-time performance requirements. About This Item We aim to show you accurate product information.


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Manufacturers, suppliers and others provide what you see here, and we have not verified it. See our disclaimer. Making task-appropriate algorithm and platform choices for large-scale machine learning requires understanding the benefits, trade-offs, and constraints of the available options"-- This book presents an integrated collection of representative approaches for scaling up machine learning and data mining methods on parallel and distributed computing platforms.

Making task-appropriate algorithm and platform choices for large-scale machine learning requires understanding the benefits, trade-offs, and constraints of the available options.

Scalable data science with R - O'Reilly Media

Extensive coverage of parallelization of boosted trees, SVMs, spectral clustering, belief propagation and other popular learning algorithms and deep dives into several applications make the book equally useful for researchers, students, and practitioners. Specifications Publisher Cambridge University Press.

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Scaling Up and Out Programming GPU Clusters with Numba and Dask - SciPy 2016 - Siu Kwan Lam

Madrid: Difusora Larousse - Editorial Tecnos, Barcelona: social UOC, Without making the data smaller through sampling, for example this problem can be solved in two different ways: Scaling-out vertically, by using a machine with more available RAM. Scaling-out horizontally: In this context, it is necessary to change the default R behaviour of loading all required data in memory and access the data differently by using a distributed or parallel schema with a divide-and-conquer or in R terms, split-apply-combine approach like MapReduce.

There is a third approach—scaling-out horizontally can be solved by using R as an interface to the most popular distributed paradigms: Hadoop: through using the set of libraries or packages known as RHadoop. These R packages allow users to analyze data with Hadoop through R code.

ISBN 10: 0521192242

They consist on rhdfs to interact with HDFS systems; rhbase to connect with HBase; plyrmr to perform common data transformation operations over large datasets; rmr2 that provides a map-reduce API; and ravro that writes and reads avro files. It provides a distributed data frame implementation that supports operations like selection, filtering, aggregation, etc.

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In the case of distributed machine learning frameworks, the most popular approaches using R, are the following: Spark MLlib: through SparkR , some of the machine learning functionalities of Spark are exported in the R package. In particular, the following machine learning models are supported from R: generalized linear model GLM , survival regression, naive Bayes and k-means.

Scalability

H2o framework : a Java-based framework that allows building scalable machine learning models in R or Python. It can run as standalone platform or with an existing Hadoop or Spark implementation.