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    Mastering Machine Learning with Spark 2.x. Harness the potential of machine learning, through spark Michal Malohlava, Alex Tellez, Max Pumperla

    (ebook) (audiobook) (audiobook) Książka w języku 1
    Mastering Machine Learning with Spark 2.x. Harness the potential of machine learning, through spark Michal Malohlava, Alex Tellez, Max Pumperla - okladka książki

    Mastering Machine Learning with Spark 2.x. Harness the potential of machine learning, through spark Michal Malohlava, Alex Tellez, Max Pumperla - okladka książki

    Mastering Machine Learning with Spark 2.x. Harness the potential of machine learning, through spark Michal Malohlava, Alex Tellez, Max Pumperla - audiobook MP3

    Mastering Machine Learning with Spark 2.x. Harness the potential of machine learning, through spark Michal Malohlava, Alex Tellez, Max Pumperla - audiobook CD

    Autorzy:
    Michal Malohlava, Alex Tellez, Max Pumperla
    Wydawnictwo:
    Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
    Ocena:
    Bądź pierwszym, który oceni tę książkę
    Stron:
    340
    Dostępne formaty:
         PDF
         ePub
         Mobi
    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. Finally, you will build different pattern mining models using MLlib, perform complex manipulation of DataFrames using Spark and Spark SQL, and deploy your app in a Spark streaming environment.

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    O autorach książki

    Michal Malohlava, creator of Sparkling Water, is a geek and the developer; Java, Linux, programming languages enthusiast who has been developing software for over 10 years. He obtained his PhD from Charles University in Prague in 2012, and post doctorate from Purdue University. During his studies, he was interested in the construction of not only distributed but also embedded and real-time, component-based systems, using model-driven methods and domain-specific languages. He participated in the design and development of various systems, including SOFA and Fractal component systems and the jPapabench control system. Now, his main interest is big data computation. He participates in the development of the H2O platform for advanced big data math and computation, and its embedding into Spark engine, published as a project called Sparkling Water.
    Alex Tellez is a life-long data hacker/enthusiast with a passion for data science and its application to business problems. He has a wealth of experience working across multiple industries, including banking, health care, online dating, human resources, and online gaming. Alex has also given multiple talks at various AI/machine learning conferences, in addition to lectures at universities about neural networks. When hes not neck-deep in a textbook, Alex enjoys spending time with family, riding bikes, and utilizing machine learning to feed his French wine curiosity!
    Max Pumperla is a data scientist and engineer specializing in deep learning and its applications. He currently works as a deep learning engineer at Skymind and is a co-founder of aetros.com. Max is the author and maintainer of several Python packages, including elephas, a distributed deep learning library using Spark. His open source footprint includes contributions to many popular machine learning libraries, such as keras, deeplearning4j, and hyperopt. He holds a PhD in algebraic geometry from the University of Hamburg.

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