Machine learning

What is AI Technical Debt? Key Risks for Machine Learning Projects

Reinforcement Learning at Scale: Engineering the Next Generation of Intelligence

Paperclip + OpenClaw : mon équipe IA tourne 24h/24

Paperclip + OpenClaw: my AI team runs 24/7

MLOps 101: Platforms and Processes for Building AI | NVIDIA GTC

Building Agentic Applications with Spring AI • Matthew Meckes • GOTO 2025

[ИАД, весна 2026] Введение в машинное обучение. Лекция 8: Метрические методы машинного обучения

[ИАД, весна 2026] Introduction to Machine Learning. Lecture 8: Metric Methods of Machine Learning

MASTERCLASS MINT 2026 | Sébastien Gerchinovitz | March 26, 2026

Les 16 commandements pour économiser des tokens et rendre l'IA plus performante

The 16 commandments for saving tokens and making AI more efficient

Machine Learning 5 [Even Semester 2025/2026 Telyu] - Exploratory Data Analysis (EDA)

Google dévoile Gemma 4 : le modèle ouvert le plus intelligent à ce jour !

Crée un chatbot téléphonique intelligent avec Genspark 3 !

Create a smart telephone chatbot with Genspark 3!

68-я Всероссийская научная конференция МФТИ, ФПМИ — Секция проблем ИАД, поток 3

68th All-Russian Scientific Conference MIPT, FPMI — Section on IAD problems, stream 3

MLT - Week 8 | Session- 2

MLT - Week 8 | Session- 2

68-я Всероссийская научная конференция МФТИ, ФПМИ — Секция проблем ИАД, поток 2

68th All-Russian Scientific Conference of MIPT, FPMS — Section on IAD Problems, stream 2

68-я Всероссийская научная конференция МФТИ, ФПМИ — Секция проблем ИАД, поток 1

68th All-Russian Scientific Conference of MIPT, FPMI — Section of IAD Problems, stream 1

MLT | Week-8 | Session-1

MLT | Week-8 | Session-1

Lec 47: Self-Supervised Learning

[ИАД, весна 2026] Введение в машинное обучение. Лекция 7: Вероятностные модели машинного обучения

[IAD, Spring 2026] Introduction to Machine Learning. Lecture 7: Probabilistic Models of Machine Learning

[IS] Quantum Machine Learning – Foundations, Methodologies, and Real-World Applications

"Efficient Finetuning of Large Language Models via Large-Width Analysis" - Soufiane Hayou

NVIDIA Nemotron Unpacked: Build, Fine-Tune, and Deploy Open Models From NVIDIA

HAI Seminar: Predicting Child Labor in Ghana's Cocoa Industry

How to finetune LLMs on custom data domains (CPT tutorial with Unsloth)

MLT | Week-7 | Session-2

MLT | Week-7 | Session-2

Universal Speech Content Factorization - Henry Li Xinyuan

World Models for AI

MLT | Week-7 | Session-1

MLT | Week-7 | Session-1

AI Foundations for Absolute Beginners

CompACT para robots, Epistemic Neural Networks para RLHF, Online Experiential Learning

CompACT para robots, Epistemic Neural Networks para RLHF, Online Experiential Learning

New Self Improving Hyperagents Break Limits Of AI

MLT | Week-6 | Session-2

MLT | Week-6 | Session-2

Ton Claude Code oublie TOUT ? Tout savoir sur sa mémoire ! (Guide complet)

Your Claude Code forgets EVERYTHING? Everything to know about its memory! (Complete guide)

[Generative AI in Urdu/Hindi] Lecture 22: Pre-training objectives of BERT

MENACE | Ironman C1.E06

MLT | Week-5 | Session-2

MLT | Week-5 | Session-2

Dr. Shruti Mishra | Reinforcement learning in fluid mechanical environments

Agent Teams Claude : automatisez tout, facturez plus!

AI Consultants Who Code Claude Team AI Agents Are Winning This New Market!

How Linear Algebra Powers Machine Learning (ML)

[ИАД, весна 2026] Введение в машинное обучение. Лекция 6: Методология машинного обучения

[IAD, spring 2026] Introduction to Machine Learning. Lecture 6: Machine Learning Methodology

Reinforcement Learning via Self-Distillation