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distill.pub | ||
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blog.fastforwardlabs.com
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| | | | | By Chris and Melanie. The machine learning life cycle is more than data + model = API. We know there is a wealth of subtlety and finesse involved in data cleaning and feature engineering. In the same vein, there is more to model-building than feeding data in and reading off a prediction. ML model building requires thoughtfulness both in terms of which metric to optimize for a given problem, and how best to optimize your model for that metric! | |
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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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iamirmasoud.com
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| | | | | Amir Masoud Sefidian | |
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edleightondick.com
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| | | On October 1, 2016, I was first honored by Microsoft with their prestigious MVP award, given to those that they feel have made a significant difference in their development communities. Many of those that had previously been named as an MVP were people that I had looked up to for years, and it was humbling [...] | ||
