AI — Models & Research

Comment les I.A. font-elles pour comprendre notre langue ?

How do AIs manage to understand our language?

Lecture 20: MIT 6.800/6.843 Robotics Manipulation (Fall 2021) | "Reinforcement Learning (Part 3)"

#033 ML Анатолий Старостин. Танцующие скелетики, генеративное искусство и философия ИИ

#033 ML Anatoly Starostin. Dancing Skeletons, Generative Art and AI Philosophy

Lecture 19: MIT 6.800/6.843 Robotics Manipulation (Fall 2021) | "How to Train your Robot"

Lecture 18: MIT 6.800/6.843 Robotics Manipulation (Fall 2021) | "Reinforcement Learning (Part 1)"

#032 MLSpec Сергей Марков. ruDALL-E. Генерация картинок по текстовому описанию

#032 MLSpec Sergey Markov. ruDALL-E. Generating images from text description

Using linear regression to estimate non-linear problems (with code!)

Lecture 17: MIT 6.800/6.843 Robotics Manipulation (Fall 2021) | "Behavior Cloning"

NEURONE ARTIFICIEL - CHAT VS CHIEN - DEEP LEARNING 6

ARTIFICIAL NEURON - CAT VS DOG - DEEP LEARNING 6

#031 ML Алексей Харламов. Как фармить золото с помощью ML на Kaggle

#031 ML Alexey Kharlamov. How to farm gold with ML on Kaggle

#030 ML Олег Найдин. Голосовые ассистенты

#030 ML Oleg Naidin. Voice Assistants

Qiskit Falll Fest CIC-IPN Mexico 2021- Introducción al Aprendizaje de Máquina Cuántico

Qiskit Fall Fest CIC-IPN Mexico 2021- Introduction to Quantum Machine Learning

PROGRAMMATION d'un NEURONE ARTIFICIEL (DEEP LEARNING 5)

PROGRAMMING an ARTIFICIAL NEURON (DEEP LEARNING 5)

Lecture 11: MIT 6.800/6.843 Robotics Manipulation (Fall 2021) | "Deep Perception (for manipulation)"

La VECTORISATION des équations - DEEP LEARNING (04)

The VECTORIZATION of equations - DEEP LEARNING (04)

Lecture 6: MIT 6.800/6.843 Robotics Manipulation (Fall 2021) | "Geometric Perception (Part 2)"

Lecture 5: MIT 6.800/6.843 Robotics Manipulation (Fall 2021) | "Geometric Perception (Part 1)"

#029 ML Антон Фролов. Электронный надмозг или системы машинного перевода

#029 ML Anton Frolov. Electronic superbrain or machine translation systems

Cette IA écrit du contenu à votre place en quelques secondes | Copy.ai

Czy mamy szanse skonstruować komputery dorównujące ludzkim mózgom?

Do we have a chance to construct computers matching human brains?

Machine Learning Practice: CoLab Interface

#028 ML Максим Купрашевич. Компьютерное зрение, амурские тигры и желтая курточка

#028 ML Maxim Kuprashevich. Computer vision, Amur tigers and a yellow jacket

#027 ML Петр Ермаков. ODS - русскоязычное сообщество дата-сайентистов

#027 ML Petr Ermakov. ODS - Russian-speaking data scientist community

reading conditional independence from the BN

LES GRADIENTS D'UN NEURONE - DEEP LEARNING (03)

THE GRADIENTS OF A NEURONE - DEEP LEARNING (03)

#026 ML Виталий Моисеев. Рекомендательные системы - как вырастить "счастье пользователей"

#026 ML Vitaly Moiseev. Recommender systems - how to grow "user happiness"

Neural Network | Neural Networks

Gradient Descent | Neural Networks

Backpropagation Algorithm | Neural Networks

LE PERCEPTRON - DEEP LEARNING (02)

Principal Component Analysis | Appearance Matching

Parametric Appearance Representation | Appearance Matching

Appearance Matching | Appearance Matching

Sequential identification of agent reliability and the Bayes factor

#025 ML Про стажировки в Яндексе и распознавание рукописных цифр

#025 ML About internships at Yandex and handwritten digit recognition

Tracking by Feature Detection | Object Tracking

Gaussian Mixture Model | Object Tracking

Change Detection | Object Tracking

Lecture 22 (From linear models to deep models) | MIT 6.832 (Underactuated Robotics), Spring 2021

Tomasi-Kanade Factorization | Structure from Motion