Daniel Whitenack - Machine Learning With Go

BIRMINGHAM - MUMBAI

Copyright 2017 Packt Publishing

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First published: September 2017

Production reference: 1210917

Published by Packt Publishing Ltd.
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ISBN 978-1-78588-210-4

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Daniel Whitenack (@dwhitena), PhD, is a trained data scientist working with Pachyderm (@pachydermIO). Daniel develops innovative, distributed data pipelines that include predictive models, data visualizations, statistical analyses, and more. He has spoken at conferences around the world (GopherCon, JuliaCon, PyCon, ODSC, Spark Summit, and more), teaches data science/engineering at Purdue University (@LifeAtPurdue), and, with Ardan Labs (@ardanlabs), maintains the Go kernel for Jupyter, and is actively helping to organize contributions to various open source data science projects.

Niclas Jern has been using Go to solve interesting problems at scale since Go 1.0. He graduated from Abo Akademi University with an MSc in computer engineering, majoring in software engineering.

He enjoys using Go and other programming languages to tackle problems, especially in the fields of data processing and machine learning. His hobbies include long walks, lifting heavy metal objects at the gym, the occasional rant at http://www.njern.co, and spending quality time with his wife and daughter.

Niclas currently works at Walkbase, a company he founded together with some of his class mates from Abo Akademi university, where he leads the engineering team, which is tackling the data processing problems that come with revolutionizing retail analytics.

Richard Townsend became the top contributor to GoLearn in 2014 (and hence is responsible for a lot of its odd behavior) while studying for his undergraduate degree at Warwick University. Since then, he's worked for a top UK technology company on everything from embedded systems to Android operating system frameworks, and currently spends his time optimizing web browsers. He still spends a significant amount of time on sentiment analysis (co-authoring two papers on it) and other natural language processing tasks like part of speech tagging often using the latest deep learning technologies.

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