Machine Learning Frontiers
Samuel FlenderReal-life lessons on ML theory, practice, and careers from a seasoned ML engineer. This is the stuff you won't learn in "standard" ML curricula.
| Platform |  | Pricing | Freemium | Publishes | Weekly |
|---|---|---|---|---|---|
| Issues | 33 | Subscribers | Read | mlfrontiers.substack.com |
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Latest Issues
Recent posts by this newsletter. Browse the email archive.
Building, Deploying, and Hosting an LLM Application
This week, I thought it would be fun to leave theory land behind us and build an actual, practical LLM application. We’re going to build CalPal, an LLM-based calorie tracker. The idea is simple: the user describes in natural language what t...
Understanding Embedding Dimensional Collapse in Recommender Systems
While embedding dimensional collapse has been observed in self-supervised learning at least since 2021 (Hua et al 2021), it has only recently raised attention in recommender systems, or more specifically, the problem of scaling up recommend...
Breaking down Tencent's Recommendation Algorithm
“Bag of tricks” papers present not a single novel idea but instead aggregate a unique set of techniques and tricks that have been found to be useful in production. In contrast to more traditional “research-style” papers with a focus on a si...
Target Attention Is All You Need: Modeling Extremely Long User Action Sequences in Recommender Systems
Recommender systems try to predict your next action, so it may not be surprising that the most important signals going into such models are user action sequences. The best recommender systems today are consequently those that can efficientl...
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Authors
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Physics PhD turned ML Engineer
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