Unlock the complexities of machine learning algorithms in Spark to generate useful data insights through this data analysis tutorial
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.
The purpose of machine
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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 he's 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.
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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