Thoughtful Machine Learning: A Test-Driven Approach (Paperback)

Matthew Kirk

  • 出版商: O'Reilly
  • 出版日期: 2014-11-11
  • 定價: $1,420
  • 售價: 9.5$1,349
  • 貴賓價: 9.0$1,278
  • 語言: 英文
  • 頁數: 236
  • 裝訂: Paperback
  • ISBN: 1449374069
  • ISBN-13: 9781449374068
  • 相關分類: TDD 測試導向開發Machine Learning
  • 立即出貨

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商品描述

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

商品描述(中文翻譯)

學習如何將測試驅動開發(TDD)應用於機器學習算法,並避免可能導致分析失敗的錯誤。在這本實用指南中,作者Matthew Kirk帶領您了解TDD和機器學習的原則,並展示如何將TDD應用於多個機器學習算法,包括Naive Bayesian分類器和神經網絡。

機器學習算法通常內置了測試,但無法應對編碼中的人為錯誤。與其像許多研究人員一樣盲目依賴機器學習結果,您可以通過TDD減少錯誤風險,並編寫乾淨、穩定的機器學習代碼。如果您熟悉Ruby 2.1,您就可以開始了。

本書內容包括:
- 在編寫代碼之前使用TDD編寫和運行測試
- 學習八種機器學習算法的最佳用途和權衡
- 通過引人入勝的實踐練習,使用真實世界的例子測試每個算法
- 理解TDD和科學方法驗證解決方案之間的相似之處
- 了解機器學習的風險,例如欠擬合和過擬合數據
- 探索改進機器學習模型或數據提取的技巧