Mastering Machine Learning with Spark 2.x
Idioma: inglés
Editorial: Packt Publishing, 2017
- Tapa blanda
- Nuevo
Librería: PBShop.store UK, Fairford, GLOS, Reino UnidoPBShop.store UK
Vendedor de AbeBooks desde 11 de junio de 1999
Condición: Nuevo
EUR 58,12
Cantidad disponible: 15 disponibles
Añadir al carritoDescripción del artículo del vendedor
New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
N° de ref. del artículo IQ-9781785283451
- Título
- Mastering Machine Learning with Spark 2.x
- Autor
- Alex Tellez
- Editorial
- Packt Publishing
- Año de publicación
- 2017
- Estado
- New
- Encuadernación
- PAP
- Idioma
- inglés
- ISBN 10
- 1785283456
- ISBN 13
- 9781785283451
- Peso del artículo
- 723 gramos
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
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Acerca del autor
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.
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.
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Vendedor de AbeBooks desde 11 de junio de 1999
Tarifas de envío de Reino Unido a Estados Unidos de America
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