00999cam0-2200313---450-99000507695040332120090526155647.0000507695FED01000507695(Aleph)000507695FED0100050769519990604d1966----km-y0itay50------bafreFRy-------001yy<<Le >>mot "rien" et ses concurrents en français(du 14. siècle a l'époque contemporaine)Robert MartinParisKlincksieck1966X, 325 p.23 cmBibliothèque française et romaneSérie AManuels et études linguistiques12Lingua franceseStoria447.009Martin,Robert<1936- >191441ITUNINARICAUNIMARCBK990005076950403321447.009 MAR 1Ist.f.m.18345FLFBCFLFBCMot "rien" et ses concurrents en français531378UNINA04017nam 2200481 450 991079653390332120170926121532.0(CKB)4100000000880849(MiAaPQ)EBC5015713(WaSeSS)IndRDA00090924(CaSebORM)9781785283451(PPN)228010705(EXLCZ)99410000000088084920170926h20172017 uy 0engurcnu||||||||rdacontentrdamediardacarrierMastering machine learning with spark 2.x create scalable machine learning applications to power a modern data-driven business using spark /Alex Tellez, Max Pumperla, Michal Malohlava1st editionBirmingham, England ;Mumbai, [India] :Packt,2017.©20171 online resource (320 pages) illustrations (some color)Includes index.1-78528-345-6 1-78528-241-7 Unlock the complexities of machine learning algorithms in Spark to generate useful data insights through this data analysis tutorial About This Book Process and analyze big data in a distributed and scalable way Write sophisticated Spark pipelines that incorporate elaborate extraction Build and use regression models to predict flight delays Who This Book Is For Are you a developer with a background in machine learning and statistics who is feeling limited by the current slow and ?small data? machine learning tools? Then this is the book for you! In this book, you will create scalable machine learning applications to power a modern data-driven business using Spark. We assume that you already know the machine learning concepts and algorithms and have Spark up and running (whether on a cluster or locally) and have a basic knowledge of the various libraries contained in Spark. What You Will Learn Use Spark streams to cluster tweets online Run the PageRank algorithm to compute user influence Perform complex manipulation of DataFrames using Spark Define Spark pipelines to compose individual data transformations Utilize generated models for off-line/on-line prediction Transfer the learning from an ensemble to a simpler Neural Network Understand basic graph properties and important graph operations Use GraphFrames, an extension of DataFrames to graphs, to study graphs using an elegant query language Use K-means algorithm to cluster movie reviews dataset In Detail The purpose of machine learning is to build systems that learn from data. Being able to understand trends and patterns in complex data is critical to success; it is one of the key strategies to unlock growth in the challenging contemporary marketplace today. With the meteoric rise of machine learning, developers are now keen on finding out how can they make their Spark applications smarter. This book gives you access to transform data into actionable knowledge. The book commences by defining machine learning primitives by the MLlib and H2O libraries. You will learn how to use Binary classification to detect the Higgs Boson particle in the huge amount of data produced by CERN particle collider and classify daily health activities using ensemble Methods for Multi-Class Classification. Next, you will solve a typical regression problem involving flight delay predictions and write sophisticated Spark pipelines. You will analyze Twitter data with help of the doc2vec algorithm and K-means clustering...Machine learningMachine learningIndustrial applicationsMachine learning.Machine learningIndustrial applications.006.31Tellez Alex1565893Pumperla MaxMalohlava MichalMiAaPQMiAaPQMiAaPQBOOK9910796533903321Mastering machine learning with spark 2.x3835982UNINA01082nam0 22002771i 450 UON0018615420231205103154.23939-00-47851-120030730d1992 |0itac50 bagerDE|||| 1||||ˆEine ‰Frau fahrt um die Weltdie reise 1846 nach Sudamerika, China, Ostindien, Persien und Kleinasien. / Ida Pfeifferherausgegeben von Gabriele HabingerViennaPromedia1992X, 333 p.21 cm.DonneStoriaSec. 18.-19.UONC034349FIFEMMINISMOStudiUONC036231FIATWienUONL003140PFEIFFERIdaUONV108198130815PromediaUONV265676650ITSOL20251128RICASIBA - SISTEMA BIBLIOTECARIO DI ATENEOUONSIUON00186154SIBA - SISTEMA BIBLIOTECARIO DI ATENEOSI EUR D A 1683 SI SC 26656 5 1683 Frau fahrt um die Welt1286440UNIOR