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 www.jeremykun.com
Weak Learning, Boosting, and the AdaBoost algorithm || Math ? Programming
| | www.let-all.com
One-Inclusion Graphs and the Optimal Sample Complexity of PAC Learning: Part 1 - The Learning Theory Alliance Blog
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| | francisbach.com
Scaling laws of optimization - Machine Learning Research Blog
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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.
| | sebastianraschka.com
Predictive modeling, supervised machine learning, and pattern classification
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| | I'm Sebastian: a machine learning & AI researcher, programmer, and author. As Staff Research Engineer Lightning AI, I focus on the intersection of AI research, software development, and large language models (LLMs).
| | randorithms.com
How to Find the Taylor Series of an Inverse Function - Randorithms
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| The Taylor series is a widely-used method to approximate a function, with many applications. Given a function \(y = f(x)\), we can express \(f(x)\) in terms ...