Machine learning

Comment CHOISIR LE BON MODÈLE de Machine Learning ?

How to CHOOSE THE RIGHT Machine Learning MODEL?

QSI Seminar: Dr Markus Heyl, Max Planck Inst., Reinforcement Learning for Digital Quantum Simulation

Learning Parity with Noise|| @ CMU || Lecture 26b of CS Theory Toolkit

Union bound and ML theory

QSI Seminar: Asst Prof Nathan Wiebe, PNNL, Training Fully Quantum Boltzmann Machines, 29/05/2020

MODÈLE DE MACHINE LEARNING - Covid19 (29/30)

MACHINE LEARNING MODEL - Covid19 (29/30)

QSI Seminar: Asst. Prof. Nana Liu, SJTU, Introducing Adversarial Quantum Learning, 22/05/2020

ENSEMBLE LEARNING : BAGGING, BOOSTING et STACKING (25/30)

ENSEMBLE LEARNING: BAGGING, BOOSTING and STACKING (25/30)

APPRENTISSAGE NON-SUPERVISÉ avec Python (24/30)

UNSUPERVISED LEARNING with Python (24/30)

FEATURE SELECTION avec SKLEARN (23/30)

FEATURE SELECTION with SKLEARN (23/30)

SKLEARN IMPUTER : NETTOYAGE DE DONNÉES

SKLEARN IMPUTE: DATA CLEANING

SKLEARN PIPELINE AVANCÉE

SKLEARN ADVANCED PIPELINE

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

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)

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

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

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

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

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

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

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?

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

Learning Embedding Space for Clustering From Deep Representations

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

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

BRAIN 2025, Prof. Terry Sejnowski

Classification