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blog.omega-prime.co.uk | ||
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dustintran.com
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| | | | | Stochastic gradient descent (SGD) has seen wide application for learning problems on large scale data, whether this be for generalized linear models [6], SVM... | |
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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 the spectral properties of kernel matrices, focusing on the analysis of eigenvalues and their estimation using tools like the matrix Bernstein inequality. It also covers the estimation of the number of integer vectors with a given L1 norm and the relationship between these counts and combinatorial structures. The post includes a detailed derivation of bounds for the difference between true and estimated eigenvalues, highlighting the role of the degrees of freedom and the impact of regularization in kernel methods. Additionally, it touches on the importance of spectral analysis in machine learning and its applications in various domains. | ||
