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where it all starts
learn machine learning foundations
data, training, evaluation, and the core algorithms every other path builds on — the right place to begin.
the curated path
curatedbeginner~4 weeks, part-time
machine learning foundations
the bedrock every ai/ml path stands on: how learning from data actually works, the core algorithms, and how to tell a good model from a lucky one.
4 modules · 12 resources · checkpoint per modulestay current
what's new in machine learning foundations
- Thermodynamic natural gradient descentintroduces a natural gradient optimizer that regulates step sizes using a physical speed-cost constraint, combining fisher-preconditioned updates with dissipation-aware control. practitioners can apply this to improve optimization stability and efficiency, especially in settings where computational cost matters.
- Deep neural operator for free boundary problemsextends neural operators to solve partial differential equations on unknown domains, addressing a class of problems traditional methods struggle with. this enables practitioners to tackle complex scientific computing problems like phase transitions and moving boundaries using learned operators.
- Exact sequence interpolation with transformersproves that transformers can exactly memorize and interpolate finite sequence datasets, establishing theoretical foundations for understanding transformer expressiveness. this clarifies what transformers are fundamentally capable of and helps practitioners reason about model capacity for sequence tasks.
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