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

Neural networks from the single perceptron to modern transformers: how depth, the right activations, and gradient-based training let a model learn its own features instead of hand-crafted ones.

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.

**Prerequisites:** the [Machine Learning](/en/Machine%20Learning) course (especially the perceptron in [Linear classification](/en/Machine%20Learning/06%20Linear%20classification)), basic Python, calculus, and linear algebra.

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.

## Syllabus

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

2. [Multilayer perceptron](/en/Deep%20Learning/02%20Multilayer%20perceptron)

3. [Activation functions](/en/Deep%20Learning/03%20Activation%20functions)

4. [Loss functions and output layers](/en/Deep%20Learning/04%20Loss%20functions%20and%20output%20layers)

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

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

7. [Initialization and vanishing gradients](/en/Deep%20Learning/07%20Initialization%20and%20vanishing%20gradients)

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

9. [Regularization and dropout](/en/Deep%20Learning/09%20Regularization%20and%20dropout)

10. [Convolutional networks](/en/Deep%20Learning/10%20Convolutional%20networks)

11. [CNN architectures](/en/Deep%20Learning/11%20CNN%20architectures)

12. [Embeddings and representation learning](/en/Deep%20Learning/12%20Embeddings%20and%20representation%20learning)

13. [Recurrent networks](/en/Deep%20Learning/13%20Recurrent%20networks)

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

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

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

17. [Deep learning in practice](/en/Deep%20Learning/17%20Deep%20learning%20in%20practice)

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