| zip | Baixar grátis |
|---|---|
| rar | Baixar grátis |
| epub | Baixar grátis |
| Baixar grátis | |
| lit | Baixar grátis |
| odf | Baixar grátis |
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.
Autores populares
Icon Group International (402) Publishing lmgdaw (341) Nikolay Krechet (266) Unknown Author (196) Lyudmil Tsvetkov (179) Barrett Williams (166) Kennie B Journals (158) Сергей Каледин (148) Riddler Books (122) ChatGPT ChatGPT (118) NAK Legal Publishing (116) Evgeniia Naumchenko (114) Sharifa McFarlane (97) Jillo Trukudo (96) Quick Witted Coconut (94) Roggie Clark (94) Mint and Cherry Notebooks (93) Marina Buchholz B.Eng (89) Iwona Makara (87) Mary Hirose (87)