Thoughtful machine learning: a test-driven approach
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 alg...
Gespeichert in:
| Beteilige Person: | |
|---|---|
| Weitere beteiligte Personen: | , , , , |
| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Sebastopol, California
O'Reilly
2015
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| Schlagwörter: | |
| Links: | https://learning.oreilly.com/library/view/-/9781449374075/?ar |
| Zusammenfassung: | 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. |
| Beschreibung: | Includes index. - Includes bibliographical references and index. - Online resource; title from PDF title page (ebrary, viewed October 11, 2014) |
| Umfang: | 1 online resource (235 pages) illustrations (some color) |
| ISBN: | 9781449374105 1449374107 1449374069 9781449374068 |
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| spelling | Kirk, Matthew VerfasserIn aut Thoughtful machine learning a test-driven approach Matthew Kirk ; Mike Loukides and Ann Spencer, editors ; Melanie Yarbrough, production editor ; Rachel Monaghan, copyeditor ; Ellie Volkhausen, cover designer Sebastopol, California O'Reilly 2015 ©2015 1 online resource (235 pages) illustrations (some color) Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Includes index. - Includes bibliographical references and index. - Online resource; title from PDF title page (ebrary, viewed October 11, 2014) 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. Computer algorithms Algorithms Algorithmes algorithms Loukides, Michael Kosta HerausgeberIn edt Spencer, Ann HerausgeberIn edt Yarbrough, Melanie HerausgeberIn edt Monaghan, Rachel HerausgeberIn edt Volkhausen, Ellie MitwirkendeR ctb 9781449374068 Erscheint auch als Druck-Ausgabe 9781449374068 |
| spellingShingle | Kirk, Matthew Thoughtful machine learning a test-driven approach Computer algorithms Algorithms Algorithmes algorithms |
| title | Thoughtful machine learning a test-driven approach |
| title_auth | Thoughtful machine learning a test-driven approach |
| title_exact_search | Thoughtful machine learning a test-driven approach |
| title_full | Thoughtful machine learning a test-driven approach Matthew Kirk ; Mike Loukides and Ann Spencer, editors ; Melanie Yarbrough, production editor ; Rachel Monaghan, copyeditor ; Ellie Volkhausen, cover designer |
| title_fullStr | Thoughtful machine learning a test-driven approach Matthew Kirk ; Mike Loukides and Ann Spencer, editors ; Melanie Yarbrough, production editor ; Rachel Monaghan, copyeditor ; Ellie Volkhausen, cover designer |
| title_full_unstemmed | Thoughtful machine learning a test-driven approach Matthew Kirk ; Mike Loukides and Ann Spencer, editors ; Melanie Yarbrough, production editor ; Rachel Monaghan, copyeditor ; Ellie Volkhausen, cover designer |
| title_short | Thoughtful machine learning |
| title_sort | thoughtful machine learning a test driven approach |
| title_sub | a test-driven approach |
| topic | Computer algorithms Algorithms Algorithmes algorithms |
| topic_facet | Computer algorithms Algorithms Algorithmes algorithms |
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