Machine learning

Computational Thinking on Learning Models

Alexander Shlimovich - Data Selection for Empirical Risk Minimization (Eng)

Self-evolving AI Agents

In Genomics ML, Accuracy Is Not the Same as Equity: Part 2

Theory of Modern AI: Learning Theoretic, Game Theoretic, and Algorithmic Perspectives

Stanford CS336 Language Modeling from Scratch | Spring 2026 | Lecture 15: Mid/Post-Training

Red Points and Blue Points

Questions for Theory in the New Age of Machine Learning

Probably Approximately Precision and Recall Learning

Enhanced and Efficient Reasoning in Large Language Models

An Occam's Razor Principle for Transformers?

MIT 6.S191: Secrets of Massively Parallel Training

J'automatise ma prospection en 5 minutes avec Claude

I automate my prospecting in 5 minutes with Claude

Generative AI L29: Low Rank Adaptation (LoRA)

Generative AI L29: Low Rank Adaptation (LoRA)

Generative AI L27: Supervised fine tuning, full fine tuning, parameter-efficient fine tuning (PEFT)

Shancong Mou - Derivative-Informed Training of Neural Operators on the Fly - IPAM at UCLA

In-context modeling as a retrain-free paradigm for foundation models || May 22, 2026

Stanford Robotics Seminar ENGR319 | Spring 2026 | Integrated Learning and Planning

Jeff Schneider - Machine Learning for Scenario Design and Plasma Control in Tokamaks

Vous gaspillez 90% de vos tokens sans le savoir

You are wasting 90% of your tokens without realizing it.

Generative AI L23: Training the transformer

Generative AI L23: Training the transformer

Artisan BTP : L'IA génère votre métré complet en quelques minutes

Construction Pros: AI Generates Your Full Bill of Quantities in Minutes

[ИАД, весна 2026] Математические методы анализа текстов. Лекция 9: Efficient Inference

[IAD, Spring 2026] Mathematical Methods for Text Analysis. Lecture 9: Efficient Inference

[ИАД, весна 2026] Математические методы анализа текстов. Лекция 7: Training LLMs

[IAD, Spring 2026] Mathematical Methods for Text Analysis. Lecture 7: Training LLMs

CRÉE-TOI un SECOND CERVEAU IA : Obsidian + Claude Code !

CREATE YOURSELF an AI SECOND BRAIN: Obsidian + Claude Code!

The biggest AI breakthrough in medicine & drug discovery

Ron Levie - Szemerédi Regularity Lemma in Graph Machine Learning (Heb)

Idan Mehalel - Sample Complexity and Mistake Bounds of Autoregressive Reasoning: CoT vs. EtE (Eng)

Aleksandar Terzić: Structured Sparse Transition Matrices to Enable State Tracking in SSMs

Il a connecté son ADN et sa montre à l'IA (le résultat est génial)

He connected his DNA and his watch to AI (the result is amazing)

Le monde en ÉTAT DE CHOC : ce robot vient de devenir moine

The world in SHOCK: this robot has just become a monk

This is The Most Powerful Tool to Give to Claude Code

MLT| end term| revision session - 2

MLT| end term| revision session - 2

Mon IA prospecte à ma place 24h/24 (je te DONNE le système) 🤖

My AI does the prospecting for me 24/7 (I'll GIVE you the system) 🤖

[ИАД, весна 2026] Введение в машинное обучение. Лекция 12: Инкрементное и онлайновое обучение

[IAD, Spring 2026] Introduction to Machine Learning. Lecture 12: Incremental and Online Learning

Master 97% of Codex in 1 Hour (full course)

[ИАД, весна 2026] Байесовский выбор моделей II. Лекция 10: Оценивание гиперпараметров граф. моделей

[IAD, spring 2026] Bayesian model selection II. Lecture 10: Estimation of hyperparameters of graphical models

RLCR, Razonamiento en espacio latente, Bitter Lesson para agentes

RLCR, Reasoning in latent space, Bitter Lesson for agents

MLP End Term Revision

MLP End Term Revision

Tim Slendebroek - From scaling laws to the design space: How we design machines we cannot yet build