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windowsontheory.org | ||
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hackmd.io
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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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blog.ml.cmu.edu
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| | | | | The latest news and publications regarding machine learning, artificial intelligence or related, brought to you by the Machine Learning Blog, a spinoff of the Machine Learning Department at Carnegie Mellon University. | |
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www.analyticsvidhya.com
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| | | Image classification using CNN and explore how to create, train, and evaluate neural networks for image classification tasks. | ||
