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Learning PySpark

You're reading from Learning PySpark

Product type Book

Published in Feb 2017

Publisher Packt

ISBN-13 9781786463708

Pages 274 pages

Edition 1st Edition

Languages

Concepts

Data Processing

Authors (2):

Tomasz Drabas

Denny Lee

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Table of Contents (20) Chapters

Learning PySpark

Credits

Foreword

About the Authors

About the Reviewer

www.PacktPub.com

Customer Feedback

Preface

1. Understanding Spark

2. Resilient Distributed Datasets

3. DataFrames

4. Prepare Data for Modeling

5. Introducing MLlib

6. Introducing the ML Package

7. GraphFrames

8. TensorFrames

9. Polyglot Persistence with Blaze

10. Structured Streaming

11. Packaging Spark Applications

Index

Summary

In this chapter, we have reviewed the fundamentals of neural networks and Deep Learning, including the components of feature engineering. With all this new excitement in Deep Learning, we introduced TensorFlow and how it can work closely together with Apache Spark through TensorFrames.

TensorFrames is a powerful deep learning tool that allows data scientists and engineers to work with TensorFlow with data stored in Spark DataFrames. This allows you to expand the capabilities of Apache Spark to a powerful deep learning toolset that is based on the learning process of neural networks. To help continue your Deep Learning journey, the following are some great TensorFlow and TensorFrames resources:

TensorFlow: https://www.tensorflow.org/
TensorFlow | Get Started: https://www.tensorflow.org/get_started/get_started
TensorFlow | Guides: https://www.tensorflow.org/tutorials/
Deep Learning on Databricks: https://databricks.com/blog/2016/12/21/deep-learning-on-databricks.html
TensorFrames (GitHub...

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Authors (2)

Tomasz Drabas

Tomasz Drabas is a Data Scientist working for Microsoft and currently residing in the Seattle area. He has over 12 years' international experience in data analytics and data science in numerous fields: advanced technology, airlines, telecommunications, finance, and consulting. Tomasz started his career in 2003 with LOT Polish Airlines in Warsaw, Poland while finishing his Master's degree in strategy management. In 2007, he moved to Sydney to pursue a doctoral degree in operations research at the University of New South Wales, School of Aviation; his research crossed boundaries between discrete choice modeling and airline operations research. During his time in Sydney, he worked as a Data Analyst for Beyond Analysis Australia and as a Senior Data Analyst/Data Scientist for Vodafone Hutchison Australia among others. He has also published scientific papers, attended international conferences, and served as a reviewer for scientific journals. In 2015 he relocated to Seattle to begin his work for Microsoft. While there, he has worked on numerous projects involving solving problems in high-dimensional feature space.

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Denny Lee

Denny Lee is a Principal Program Manager at Microsoft for the Azure DocumentDB teamMicrosoft's blazing fast, planet-scale managed document store service. He is a hands-on distributed systems and data science engineer with more than 18 years of experience developing Internet-scale infrastructure, data platforms, and predictive analytics systems for both on-premise and cloud environments. He has extensive experience of building greenfield teams as well as turnaround/ change catalyst. Prior to joining the Azure DocumentDB team, Denny worked as a Technology Evangelist at Databricks; he has been working with Apache Spark since 0.5. He was also the Senior Director of Data Sciences Engineering at Concur, and was on the incubation team that built Microsoft's Hadoop on Windows and Azure service (currently known as HDInsight). Denny also has a Masters in Biomedical Informatics from Oregon Health and Sciences University and has architected and implemented powerful data solutions for enterprise healthcare customers for the last 15 years.

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