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blog.omega-prime.co.uk | ||
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bdtechtalks.com
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| | | | | Gradient descent is the main technique for training machine learning and deep learning models. Read all about it. | |
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fa.bianp.net
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| | | | | Most proofs in optimization consist in using inequalities for a particular function class in some creative way. This is a cheatsheet with inequalities that I use most often. It considers class of functions that are convex, strongly convex and $L$-smooth. MathJax.Hub.Config({ extensions: ["tex2jax.js"], jax: ["input/TeX ... | |
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francisbach.com
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| | | | | [AI summary] The blog post discusses non-convex quadratic optimization problems and their solutions, including the use of strong duality, semidefinite programming (SDP) relaxations, and efficient algorithms. It highlights the importance of these problems in machine learning and optimization, particularly for non-convex problems where strong duality holds. The post also mentions the equivalence between certain non-convex problems and their convex relaxations, such as SDP, and provides examples of when these relaxations are tight or not. Key concepts include the role of eigenvalues in quadratic optimization, the use of Lagrange multipliers, and the application of methods like Newton-Raphson for solving these problems. The author also acknowledges contributions... | |
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curatedsql.com
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| | | [AI summary] A collection of SQL-related blog posts from January 4, 2024, discussing topics like data manipulation, database management, and SQL Server features. | ||
