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William A Andrews

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On September 4, 2012 The Introduction to “Machine Learning in Javascript” post provides a nice introduction and context for this post and the rest of the series. I like starting my machine learning classes with genetic algorithms (which we’ll abbreviate “GA” sometimes). Genetic algorithms are probably the least practical of the ML algorithms I cover, but I love starting with them because they’re fascinating and they do a good job of introducing the “cost function” or “error function”, and the idea of local and global optima — concepts both important and common to most other ML algorithms. Genetic algorithms are inspired by nature and evolution, which is seriously cool to me. It’s no surprise, either, that artificial neural networks (“NN”) are also modeled from biology: evolution is the best general-purpose learning algorithm we’ve experienced, and the brain is the best general-purpose problem solver we know. Machine Learning: Genetic Algorithms Part 1 (Javascript) | Burak Kanber's Blog Machine Learning: Genetic Algorithms Part 1 (Javascript) | Burak Kanber's Blog
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