AI — Models & Research

SKLEARN IMPUTER : NETTOYAGE DE DONNÉES

SKLEARN IMPUTE: DATA CLEANING

SKLEARN PIPELINE AVANCÉE

SKLEARN ADVANCED PIPELINE

PYTHON SKLEARN PRE-PROCESSING + PIPELINE (22/30)

Sklearn make_scorer tutoriel : Créer vos propres métriques.

Sklearn make_scorer tutorial: Create your own metrics.

MÉTRIQUES de RÉGRESSIONS en DATA SCIENCE (Coefficient de Détermination, Erreur Quadratique, etc... )

REGRESSION METRICS in DATA SCIENCE (Coefficient of Determination, Squared Error, etc.)

CROSS-VALIDATION SKLEARN PYTHON (Techniques expliquées en Français)

SKLEARN PYTHON CROSS-VALIDATION (Techniques explained in French)

[CESERH] CI La généalogie des formes dans la perspective du Deep Learning 8/11/2019 [2/9]

[CESERH] CI The genealogy of forms in the Deep Learning perspective 8/11/2019 [2/9]

[CESERH] CI La généalogie des formes dans la perspective du Deep Learning 7/11/2019 [7/11]

[CESERH] CI The genealogy of forms from the Deep Learning perspective 7/11/2019 [7/11]

PYTHON SKLEARN - MODEL SELECTION : Train_test_split, Cross Validation, GridSearchCV (21/30)

PYTHON SKLEARN: KNN, LinearRegression et SUPERVISED LEARNING (20/30)

Mind-Machine Interfaces

RÉGRESSION LINÉAIRE MULTIPLE AVEC NUMPY - ML#9

MULTIPLE LINEAR REGRESSION WITH NUMPY - ML#9

RÉGRESSION LINÉAIRE NUMPY - ML#8

NUMPY LINEAR REGRESSION - ML#8

PYTHON : L'ESSENTIEL POUR MACHINE LEARNING - ML#7

PYTHON: THE ESSENTIAL FOR MACHINE LEARNING - ML#7

RÉGRESSION LINÉAIRE (partie 2/2) - ML#6

LINEAR REGRESSION (part 2/2) - ML#6

MATRICES ET NUMPY - ML#5

DESCENTE DE GRADIENT (GRADIENT DESCENT) - ML#4

GRADIENT DESCENT - ML#4

LA RÉGRESSION LINÉAIRE (partie 1/2) - ML#3

LINEAR REGRESSION (part 1/2) - ML#3

Mocap as a Service: Video Motion Capture Makes Human Motion Analysis for Everyone

Lecture 23 | MIT 6.832 (Underactuated Robotics), Spring 2019

Lecture 22 | MIT 6.832 (Underactuated Robotics), Spring 2019

COMMENT FONCTIONNE LE MACHINE LEARNING ?

HOW DOES MACHINE LEARNING WORK?

Fuzzy & Annotated Semantic Web Languages

Uncertainty-aware Food Analysis by Deep Learning

Neuromorphic computing with emerging memory devices

Human Authentication Using Facial Cues

From Research to Scalable AI Solutions, 2 examples: Trust & Fairness and CNN Automatic Generation

Deep Learning for SPECT, SPECT/CT and PET

Learning Embedding Space for Clustering From Deep Representations

A Survey of Attacks Against Twitter Spam Detectors in an Adversarial Environment

La théorie de l'apprentissage de Vapnik et les progrès récents de l'intelligence artificielle

Vapnik's learning theory and recent advances in artificial intelligence

Lecture 21 | MIT 6.832 (Underactuated Robotics), Spring 2018

Lecture 20 | MIT 6.832 (Underactuated Robotics), Spring 2018

Backpropagation, intuitively | Deep Learning Chapter 3

Backpropagation calculus | Deep Learning Chapter 4

Gradient descent, how neural networks learn | Deep Learning Chapter 2

But what is a neural network? | Deep learning chapter 1

BRAIN 2025, Prof. Terry Sejnowski

Le deep learning

Deep learning

Defaul Logic