𝔖 Bobbio Scriptorium
✦   LIBER   ✦

Thoughtful Machine Learning: A Test-Driven Approach

✍ Scribed by Kirk, Matthew;Loukides, Michael Kosta;Monaghan, Rachel;Spencer, Ann;Volkhausen, Ellie;Yarbrough, Melanie


Publisher
O'Reilly Media
Year
2015
Tongue
English
Weight
974 KB
Category
Fiction
City
Sebastopol, California
ISBN
1449374107

No coin nor oath required. For personal study only.

✦ Synopsis


Learn how to apply test-driven development (TDD) to machine-learning algorithmsβ€”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.

β€’ Apply TDD to write and run tests before you start coding

β€’ Learn the best uses and tradeoffs of eight machine learning algorithms

β€’ Use real-world examples to test each algorithm through engaging, hands-on exercises

β€’ Understand the similarities between TDD and the scientific method for validating solutions

β€’ Be aware of the risks of machine learning, such as underfitting and overfitting data

β€’ Explore techniques for improving your machine-learning models or data extraction

✦ Subjects


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