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Clojure for Data Science

You're reading from Clojure for Data Science

Product type Book

Published in Sep 2015

Publisher

ISBN-13 9781784397180

Pages 608 pages

Edition 1st Edition

Languages

Concepts

Author (1):

Henry Garner

Table of Contents (18) Chapters

Clojure for Data Science

Credits

About the Author

Acknowledgments

About the Reviewer

www.PacktPub.com

Preface

1. Statistics

2. Inference

3. Correlation

4. Classification

5. Big Data

6. Clustering

7. Recommender Systems

8. Network Analysis

9. Time Series

10. Visualization

Index

Scaling gradient descent with Hadoop

The length of time each iteration of batch gradient descent takes to run is determined by the size of your data and by how many processors your computer has. Although several chunks of data are processed in parallel, the dataset is large and the processors are finite. We've achieved a speed gain by performing calculations in parallel, but if we double the size of the dataset, the runtime will double as well.

Hadoop is one of several systems that has emerged in the last decade which aims to parallelize work that exceeds the capabilities of a single machine. Rather than running code across multiple processors, Hadoop takes care of running a calculation across many servers. In fact, Hadoop clusters can, and some do, consist of many thousands of servers.

Hadoop consists of two primary subsystems— the Hadoop Distributed File System (HDFS)—and the job processing system, MapReduce. HDFS stores files in chunks. A given file may be composed of many chunks and chunks...

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Authors (1)

Henry Garner

Henry Garner is a graduate from the University of Oxford and an experienced developer, CTO, and coach. He started his technical career at Britain's largest telecoms provider, BT, working with a traditional data warehouse infrastructure. As a part of a small team for 3 years, he built sophisticated data models to derive insight from raw data and use web applications to present the results. These applications were used internally by senior executives and operatives to track both business and systems performance. He then went on to co-found Likely, a social media analytics start-up. As the CTO, he set the technical direction, leading to the introduction of an event-based append-only data pipeline modeled after the Lambda architecture. He adopted Clojure in 2011 and led a hybrid team of programmers and data scientists, building content recommendation engines based on collaborative filtering and clustering techniques. He developed a syllabus and copresented a series of evening classes from Likely's offices for professional developers who wanted to learn Clojure. Henry now works with growing businesses, consulting in both a development and technical leadership capacity. He presents regularly at seminars and Clojure meetups in and around London.

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