Warrens toolchain
★
★
★
★
☆
4/5
IA — Modèles & Recherche
MLT | Week-4 | Session-2
★
★
★
★
☆
4/5
Live coding with OpenSpec
★
★
★
☆
☆
3/5
GPT 5.4 is so cracked
★
★
★
★
☆
4/5
MLP Live session
★
★
★
☆
☆
3/5
Le "Q-Day" approche ... et PERSONNE n'est prêt.
★
★
★
☆
☆
3/5
I BUILT A FULLY AUTOMATIC MANSPLAINER
★
★
★
★
☆
4/5
![[Generative AI in Urdu/Hindi] Lecture 17: Transformers (first pass)](https://i.ytimg.com/vi/-FEuUtT_T_E/maxresdefault.jpg)
MLT | Week-4 | Session-1
★
★
★
★
☆
4/5
MLP Live session Week 4
★
★
★
☆
☆
3/5
AI@UCI Workshop 3/4/26 Reinforcement Learning
★
★
★
☆
☆
3/5
![[ИАД, весна 2026] Введение в машинное обучение. Лекция 4: Эволюция идей машинного обучения](https://i.ytimg.com/vi/7kDbKYmhMAw/sddefault.jpg)
They solved AI hallucinations!
★
★
★
★
☆
4/5
![[ИАД, весна 2026] Математические методы анализа текстов. Лекция 4: Transfer Learning, BERT-like, LLM](https://i.ytimg.com/vi/66rXuGgv9N0/sddefault.jpg)
The NEW Nano Banana 2 + Claude Code = $10k Websites
★
★
★
★
☆
4/5
The Dangerous Illusion of AI Coding? - Jeremy Howard
★
★
★
★
★
5/5
Keynote - What if AGI? (Prof. Richard Susskind)
★
★
★
★
☆
4/5
Keynote - Era of experience (Prof. David Silver)
★
★
★
★
☆
4/5
![[ИАД, весна 2026] Рекомендательные системы. Лекция 2](https://i.ytimg.com/vi/okpEcNe1-f8/sddefault.jpg)
[ИАД, весна 2026] Рекомендательные системы. Лекция 2
★
★
★
★
☆
4/5
MENACE | EP04
★
★
★
☆
☆
3/5
Машинное обучение и семантический анализ
★
★
★
★
☆
4/5
![Machine Learning 1 [Even Semester 2025/2026 Telyu] - Introduction to Machine Learning](https://i.ytimg.com/vi/Lwh4YXJp6mM/maxresdefault.jpg)
![Deep Learning 1 [Even Semester 2025/2026 Telyu] - Introduction to Deep Learning](https://i.ytimg.com/vi/lnm3ACBWLmg/maxresdefault.jpg)
![[Generative AI in Urdu/Hindi] Lecture 16: attention, drawbacks of RNN & LSTM, intro. to transformers](https://i.ytimg.com/vi/5Z11FbrWswk/maxresdefault.jpg)
What is an explanation in machine learning?
★
★
★
★
☆
4/5
Shapley Additive Explanations (SHAP)
★
★
★
★
☆
4/5
Is AI Hiding Its Full Power? With Geoffrey Hinton
★
★
★
★
★
5/5
Intrinsically interpretable models
★
★
★
★
☆
4/5
Gradient-based explanations (saliency maps)
★
★
★
★
☆
4/5
Counterfactual explanations
★
★
★
★
☆
4/5
