02509nam 2200457 450 991015515880332120170106100728.01-4919-6455-31-4919-6459-6(CKB)3710000000964655(MiAaPQ)EBC4758070(WaSeSS)IndRDA00091547(CaSebORM)9781491964590(EXLCZ)99371000000096465520161216h20172017 uy 0engurcnu||||||||rdacontentrdamediardacarrierPractical machine learning with H2o powerful, scalable techniques for deep learning and ai /Darren CookFirst edition.Beijing, [China] :O'Reilly,2017.©20171 online resource (300 pages) illustrationsIncludes index.1-4919-6460-X 1-4919-6457-X Machine learning has finally come of age. With H2O software, you can perform machine learning and data analysis using a simple open source framework that’s easy to use, has a wide range of OS and language support, and scales for big data. This hands-on guide teaches you how to use H20 with only minimal math and theory behind the learning algorithms. If you’re familiar with R or Python, know a bit of statistics, and have some experience manipulating data, author Darren Cook will take you through H2O basics and help you conduct machine-learning experiments on different sample data sets. You’ll explore several modern machine-learning techniques such as deep learning, random forests, unsupervised learning, and ensemble learning. Learn how to import, manipulate, and export data with H2O Explore key machine-learning concepts, such as cross-validation and validation data sets Work with three diverse data sets, including a regression, a multinomial classification, and a binomial classification Use H2O to analyze each sample data set with four supervised machine-learning algorithms Understand how cluster analysis and other unsupervised machine-learning algorithms workMachine learning with H2OMachine learningDevelopmentMachine learningDevelopment.006.31Cook Darren1213667MiAaPQMiAaPQMiAaPQBOOK9910155158803321Practical machine learning with H2o2802880UNINA