Deep Learning - blame (e0287e)

Blame

36084c	lugonthier	2026-07-02 14:39:19
Add new content and images for Linear Models, Regularization, SVMs, and Decision Trees - Added images for linear regression, logistic regression, and perceptron. - Introduced a new section on Regularization and High-Dimensional Inference with detailed explanations and images. - Added content on Support Vector Machines, including definitions, loss functions, and kernel methods. - Created a new section on Decision Trees and Ensemble Methods, covering CART, bagging, random forests, and boosting. - Included relevant images to illustrate concepts in Decision Trees and Ensemble Methods.

# Deep Learning

Les réseaux de neurones, du simple perceptron aux transformeurs modernes : comment la profondeur, les bonnes fonctions d'activation et l'entraînement par gradient permettent à un modèle d'apprendre ses propres caractéristiques au lieu de les concevoir à la main.

e0287e	lugonthier	2026-07-02 15:52:15
Add new content and images for machine learning and mathematics modules - Added images for regularization and high-dimensional inference. - Introduced Support Vector Machines (SVM) module with detailed explanations and images. - Created Decision Trees and Ensemble Methods module with comprehensive content and illustrations. - Added a Mathematics overview module and a refresher on mathematical concepts essential for machine learning. - Included SVG diagrams for Bayes' rule and multivariate Gaussian distribution.

**Prérequis :** le cours [Machine Learning](/fr/Machine%20Learning) (en particulier le perceptron dans [Classification linéaire](/fr/Machine%20Learning/06%20Linear%20classification)), Python de base, calcul différentiel et algèbre linéaire.

36084c	lugonthier	2026-07-02 14:39:19
Add new content and images for Linear Models, Regularization, SVMs, and Decision Trees - Added images for linear regression, logistic regression, and perceptron. - Introduced a new section on Regularization and High-Dimensional Inference with detailed explanations and images. - Added content on Support Vector Machines, including definitions, loss functions, and kernel methods. - Created a new section on Decision Trees and Ensemble Methods, covering CART, bagging, random forests, and boosting. - Included relevant images to illustrate concepts in Decision Trees and Ensemble Methods.

## Programme

1. [Introduction](/fr/Deep%20Learning/01%20Introduction)

2. [Perceptron multicouche](/fr/Deep%20Learning/02%20Multilayer%20perceptron)

3. [Fonctions d'activation](/fr/Deep%20Learning/03%20Activation%20functions)

4. [Fonctions de perte et couches de sortie](/fr/Deep%20Learning/04%20Loss%20functions%20and%20output%20layers)

5. [Rétropropagation](/fr/Deep%20Learning/05%20Backpropagation)

6. [Optimisation](/fr/Deep%20Learning/06%20Optimization)

7. [Initialisation et disparition du gradient](/fr/Deep%20Learning/07%20Initialization%20and%20vanishing%20gradients)

8. [Normalisation](/fr/Deep%20Learning/08%20Normalization)

9. [Régularisation et dropout](/fr/Deep%20Learning/09%20Regularization%20and%20dropout)

10. [Réseaux convolutifs](/fr/Deep%20Learning/10%20Convolutional%20networks)

11. [Architectures de CNN](/fr/Deep%20Learning/11%20CNN%20architectures)

12. [Plongements et apprentissage de représentations](/fr/Deep%20Learning/12%20Embeddings%20and%20representation%20learning)

13. [Réseaux récurrents](/fr/Deep%20Learning/13%20Recurrent%20networks)

14. [LSTM et GRU](/fr/Deep%20Learning/14%20LSTM%20and%20GRU)

15. [Attention](/fr/Deep%20Learning/15%20Attention)

16. [Transformeurs](/fr/Deep%20Learning/16%20Transformers)

17. [Le deep learning en pratique](/fr/Deep%20Learning/17%20Deep%20learning%20in%20practice)

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[Machine Learning](/fr/Machine%20Learning) · [MLOps](/fr/MLOps) · [Accueil](/fr)