Thoughtful Machine Learning

A Test-Driven Approach

Specificaties
Paperback, 234 blz. | Engels
O'Reilly | e druk, 2014
ISBN13: 9781449374068
Rubricering
O'Reilly e druk, 2014 9781449374068
Verwachte levertijd ongeveer 16 werkdagen

Samenvatting

Learn how to apply test-driven development (TDD) to machine-learning algorithms - and catch mistakes that could sink your analysis. In this practical guide, author Matthew Kirk takes you through the principles of TDD and machine learning, and shows you how to apply TDD to several machine-learning algorithms, including Naive Bayesian classifiers and Neural Networks. Machine-learning algorithms often have tests baked in, but they can't account for human errors in coding. Rather than blindly rely on machine-learning results as many researchers have, you can mitigate the risk of errors with TDD and write clean, stable machine-learning code. If you're familiar with Ruby 2.1, you're ready to start. Apply TDD to write and run tests before you start coding Learn the best uses and tradeoffs of eight machine learning algorithms Use real-world examples to test each algorithm through engaging, hands-on exercises Understand the similarities between TDD and the scientific method for validating solutions Be aware of the risks of machine learning, such as underfitting and overfitting data Explore techniques for improving your machine-learning models or data extraction

Specificaties

ISBN13:9781449374068
Taal:Engels
Bindwijze:paperback
Aantal pagina's:234
Uitgever:O'Reilly
Verschijningsdatum:7-10-2014

Over Matthew Kirk

Matthew Kirk has always been “the math guy” to those that know him best. He started his career as a quantitative financial analyst with Parametric Portfolio. While there, he studied momentum and reversal effects in Emerging Markets and optimized their 30 billion dollarportfolio. He left the finance industry to build the current version of Wetpaint.com, an entertainment website that is visited by over 10 million unique visitors each month. One of hisaccomplishments while there was the initial prototype of their patent pending Social Publishing Platform, which optimizes their publication strategy for Facebook posting. He left Wetpaint to work with a small startup in Kansas City called SocialVolt as their Chief Scientist. While there, he worked on sentiment analysis tools and spam filtering of social media data. In 2012 he started Modulus 7, which is a data science and startup consulting firm. His clients have included Ritani, The Clymb, Siren, Sqoop, and many others. Matthew holds a B.S. in Economics and a B.S. in Applied and Computational Mathematical Sciences with a concentration in Quantitative Economics from the University of Washington. He is also studying for his M.S. in Computer Science at the Georgia Institute of Technology. He has spoken around the world about using machine learning and data science with Ruby. When he’s not working, he enjoys listening to his 2000+ vinyl record collection on his Thorens TD160 Mk2 turntable.

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        Thoughtful Machine Learning