AI — Models & Research

Sequential Drift Detection

Logistic Regression

Introduction to Classifiers

Example: First Movement Behavior Classifier

Example: Computing Classifier Metrics

Example: Classes in the Infant Kinematic Data

Cross Validation

Classifier Performance Measures

Representing Data for Classifiers

Pipeline Class Example: Computing Derivatives

Multi-class Classification

Machine Learning Practice: Getting Connected to CoLab

Machine Learning Practice: Getting Connected to CoLab

Implementing Data Processing Pipelines

Example: Using Cross Validation

Example: Multi Class Classification

Drift Detection and ML Solution Retraining (4/4)

Drift Detection and ML Solution Retraining (3/4)

Drift detection and ML Solution retraining part (1/4)

(Drift detection and ML Solution retraining part (2/4

What is Dalle 2? The Dark Side of Ai Art Breakthrough Explained

Bayesian network revisited

RÉSEAU DE NEURONES PROFOND - DEEP LEARNING 10

DEEP NEURAL NETWORK - DEEP LEARNING 10

Mitigating Adversarial Text Perturbation

Detecting model drift using polynomial relations

Detecting Concept Drift in the Presence of Sparsity A Case Study of Automated Change Risk

Data dependent priors for domain adaptation bounds

A Unified Perspective on Adversarial and Out of Distribution Detection in the Open World

Lecture 14: MIT 6.832 Underactuated Robotics (Spring 2022) | "Direct Policy Search"

PROGRAMMER UN RÉSEAU DE NEURONES à 2 COUCHES - DEEP LEARNING 9

PROGRAMMING A 2-LAYER NEURAL NETWORK - DEEP LEARNING 9

Lecture 6: MIT 6.832 Underactuated Robotics (Spring 2022) | "Dynamic Programming III"

Fourier, Decision Trees, Learning Algorithms || @ CMU || Recitation 5 of CS Theory Toolkit

La BACK-PROPAGATION - DEEP LEARNING 8

BACK-PROPAGATION - DEEP LEARNING 8

Solving Wordle using information theory

RÉSEAU DE NEURONES (2 COUCHES) - DEEP LEARNING 7

NEURAL NETWORK (2 LAYERS) - DEEP LEARNING 7

How can ERM be fooled?

Technological Telepathy

#036 ML Борис Шарчилев. Как пройти собеседование в ML-команду

#036 ML Boris Sharchilev. How to pass an interview for an ML team

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