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blog.quipu-strands.com | ||
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francisbach.com
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| | | | | [AI summary] This text discusses the scaling laws of optimization in machine learning, focusing on asymptotic expansions for both strongly convex and non-strongly convex cases. It covers the derivation of performance bounds using techniques like Laplace's method and the behavior of random minimizers. The text also explains the 'weird' behavior observed in certain plots, where non-strongly convex bounds become tight under specific conditions. The analysis connects theoretical results to practical considerations in optimization algorithms. | |
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distill.pub
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| | | | | How to tune hyperparameters for your machine learning model using Bayesian optimization. | |
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www.oranlooney.com
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| | | | | [AI summary] The article discusses unsupervised learning, focusing on the Gaussian Mixture Model (GMM) and the Expectation-Maximization (EM) algorithm. It explains how GMM uses the EM algorithm to cluster data without labeled examples, and demonstrates its application on the Iris dataset. While GMM successfully finds clusters, the agreement with true labels is only 96% for one random seed, with variability across trials. The article highlights the limitations of unsupervised learning, including arbitrary complexity parameters, lack of hard metrics, and subjective model interpretation. It concludes that unsupervised learning is valuable for exploratory analysis and representation learning but requires more expertise and domain input compared to supervised met... | |
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perlhacks.com
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| | | In 2011 I wrote a series of three articles about Modern Perl programming for Linux Format. They concentrated on DBIx::Class and Dancer. | ||
