From "Umwelt" to "World" models
★
★
★
★
☆
4/5
Apprentissage par la machine
Thema UYQM
Edward Lockhart - Why AI Needs Formal Mathematics
★
★
★
★
☆
4/5
Bridging the gap between Minds & Machines
★
★
★
★
☆
4/5
Talk by Alison Gopnik (UC Berkeley)
★
★
★
★
☆
4/5
![[Machine Learning in Urdu/Hindi] 001- Introduction to Machine Learning -W1L2-HQ](https://i.ytimg.com/vi/6pM7N6NypcI/maxresdefault.jpg)
Agentic AI: L4 Part 2, Adjusting model parameters
★
★
★
☆
☆
3/5
Talk by Pranjal Awasthi (Google)
★
★
★
★
☆
4/5
Efficient Algorithms for Reliable Machine Learning
★
★
★
★
☆
4/5
Data Selection for Empirical Risk Minimization
★
★
★
★
☆
4/5
Computational Thinking on Learning Models
★
★
★
★
☆
4/5
Self-evolving AI Agents
★
★
★
★
☆
4/5
Red Points and Blue Points
★
★
★
★
☆
4/5
Probably Approximately Precision and Recall Learning
★
★
★
★
☆
4/5
An Occam's Razor Principle for Transformers?
★
★
★
★
☆
4/5
MIT 6.S191: Secrets of Massively Parallel Training
★
★
★
★
★
5/5
Generative AI L29: Low Rank Adaptation (LoRA)
★
★
★
★
☆
4/5
Vous gaspillez 90% de vos tokens sans le savoir
★
★
★
★
☆
4/5
Generative AI L23: Training the transformer
★
★
★
★
☆
4/5
![[ИАД, весна 2026] Математические методы анализа текстов. Лекция 9 Efficient Inference: от 14.04.2026](https://i.ytimg.com/vi/K9mAZGuvvx0/maxresdefault.jpg)
![[ИАД, весна 2026] Математические методы анализа текстов. Лекция 7: Training LLMs от 31.03.2026](https://i.ytimg.com/vi/Z7ccnIxJCPI/maxresdefault.jpg)
