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liorsinai.github.io | ||
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matthewmcateer.me
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| | | | | Important mathematical prerequisites for getting into Machine Learning, Deep Learning, or any of the other space | |
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alexhwilliams.info
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| | | | | [AI summary] A technical blog post explaining the mathematical foundations of Principal Component Analysis (PCA), its various generalizations like Sparse and Non-negative Matrix Factorization, and practical considerations for choosing components and handling missing data. | |
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www.jeremykun.com
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| | | | | The singular value decomposition (SVD) of a matrix is a fundamental tool in computer science, data analysis, and statistics. It's used for all kinds of applications from regression to prediction, to finding approximate solutions to optimization problems. In this series of two posts we'll motivate, define, compute, and use the singular value decomposition to analyze some data. (Jump to the second post) I want to spend the first post entirely on motivation and background. | |
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sriku.org
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| | | [AI summary] The article explains how to generate random numbers that follow a specific probability distribution using a uniform random number generator, focusing on methods involving inverse transform sampling and handling both continuous and discrete cases. | ||
