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Machine Theory

Thoughtful machine learning : a test-driven approach by Kirk, Matthew; Loukides, Michael Kosta; Monaghan, Rachel;

By Kirk, Matthew; Loukides, Michael Kosta; Monaghan, Rachel; Spencer, Ann; Volkhausen, Ellie; Yarbrough, Melanie

Learn tips on how to observe test-driven improvement (TDD) to machine-learning algorithms—and trap error that can sink your research. during this functional advisor, writer Matthew Kirk takes you thru the rules of TDD and computing device studying, and indicates you the way to use TDD to numerous machine-learning algorithms, together with Naive Bayesian classifiers and Neural Networks.

Machine-learning algorithms frequently have checks baked in, yet they can’t account for human blunders in coding. instead of blindly depend upon machine-learning effects as many researchers have, you could mitigate the danger of blunders with TDD and write fresh, solid machine-learning code. If you’re conversant in Ruby 2.1, you’re able to start.

  • Apply TDD to put in writing and run exams sooner than you begin coding
  • Learn the simplest makes use of and tradeoffs of 8 computer studying algorithms
  • Use real-world examples to check each one set of rules via enticing, hands-on exercises
  • Understand the similarities among TDD and the clinical strategy for validating solutions
  • Be conscious of the hazards of computer studying, similar to underfitting and overfitting data
  • Explore ideas for bettering your machine-learning versions or facts extraction

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Additional info for Thoughtful machine learning : a test-driven approach

Sample text

In this quick introduction, we’ll go over what exactly machine learning is, as well as a general framework for thinking about machine learning algorithms. What Is Machine Learning? Machine learning is the intersection between theoretically sound computer science and practically noisy data. Essentially, it’s about machines making sense out of data in much the same way that humans do. Machine learning is a type of artificial intelligence whereby an algorithm or method will extract patterns out of data.

Table 3-3. Lighting for the room Light 1 on? Light 2 on? Light 3 on? Light enough? No No No No Yes No No No No Yes No No No No Yes No Yes Yes No Yes No Yes Yes Yes Yes No Yes Yes Yes Yes Yes Yes In this case, we have one feature—whether the room is light enough, which is based on the three dimensions of the lights being on or off. The instances are just the combi‐ nations of lights. The Face Class The Face class has one responsibility: to load an avatar image and extract features from it. These features will then interface with the Neighborhood class, which we will discuss in the next section.

24 | Chapter 3: K-Nearest Neighbors Classification Classification and Regression Note that in the preceding scenario we are mainly looking for whether the house is happy; that is, instead of trying to value the happiness, we are simply checking whether it meets our criteria. This is called a classification problem, and it can take many forms. Many times classification problems are binary, meaning that they have only two pos‐ sible answers, such as good or bad, true or false, and right or wrong.

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