Machine learning

Stanford CS229 Machine Learning | Spring 2026 | Lecture 6: Dataset Split, ML Advice

Stanford CS229 Machine Learning | Spring 2026 | Lecture 5: Gaussian Discriminant Analysis

Lec 18: First steps in performance

Lec 17: Data loaded & Feeding the model efficiently

Stanford CS229 Machine Learning | Spring 2026 | Lecture 4: Exponential Family, GLMs Classification

Stanford CS229 Machine Learning | Spring 2026 | Lecture 3: Weighted Least Squares

Stanford CS229 Machine Learning | Spring 2026 | Lecture 2: Supervised Learning Setup

Stanford CS229 Machine Learning | Spring 2026 | Lecture 1: Introduction

How to Train Open Models with RL on Prime Intellect | Nemotron Labs

Week 5 &6 - Solve with us (Additional session)

MLT | Week-6 | Summary Session

MLT | Week-6 | Summary Session

Everything About Machine Learning Explained Slowly (For Sleep)

Yash Sarrof: Length Generalization of Transformers with a Growing Test time Alphabet

Peter Shaw: Asymptotically Optimal Description Length Objectives for Transformers

MLT | Week-5 | Summary Session

MLT | Week-5 | Summary Session

Idan Attias: On the Hardness of Learning Regular Expressions

Is Fine-Tuning Still Needed? LLMs, RAG, & LoRA

MLT | Week-3 and 4 | Revision Session-2

MLT | Week-3 and 4 | Revision Session-2

Learning machines by Piyush Srivastava

Hackathon 3 - ML‑Enhanced Analysis of EELS - Slautin

Hackathon 2 - Data curation and unsupervised analysis of the diffraction data - Houston

Day 5 - Reward Functions for Decision Making - Kalinin

Day 5 - Principles of Gaussian Processes and Bayesian Optimization - Kalinin

Day 4 - ML-based tuning and drift correction - Sgouralis

Day 4 - Introduction to Workflows in Machine Learning - Kalinin

Day 3 - Machine Learning of Spectroscopic Datasets - Kalinin

Day 1 - Welcome And Introduction

Post-Train NVIDIA Cosmos 3 In a Day with NVIDIA TAO Agent Skills | Cosmos Labs

Le premier modèle d’IA de Mira Murati repose sur l’approche chinoise... Incroyable

Mira Murati's first AI model relies on the Chinese approach... Incredible

How to Run RL Autoresearch with Agent Skills | Nemotron Labs

Alex Damian - Understanding Optimization in Deep Learning with Central Flows

Training incluyendo fracasos, Mano robótica de 1X, Oak Lab de Richard Sutton

Training including failures, 1X robotic hand, Richard Sutton's Oak Lab

Reinforcement Learning 2026 - Session 27

Reinforcement Learning 2026 - Session 27

Reinforcement Learning 2026 - Session 26

Reinforcement Learning 2026 - Session 26

Reinforcement Learning 2026 - Session 25

Reinforcement Learning 2026 - Session 25

Reinforcement Learning 2026 - Session 24

Reinforcement Learning 2026 - Session 24

Reinforcement Learning 2026 - Session 23

Reinforcement Learning 2026 - Session 23

Reinforcement Learning 2026 - Session 22

Reinforcement Learning 2026 - Session 22

Reinforcement Learning 2026 - Session 21

Reinforcement Learning 2026 - Session 21

AI Models On The Edge