Deep learning

ECTS : 2

Enseignant responsable : GABRIEL TURINICI

Volume horaire : 18

Description du contenu du cours :

1/ Deep learning: major applications, key references, general background

2/ Types of approaches: supervised, reinforcement, unsupervised

3/ Neural networks: presentation of the main components—neurons, operations, loss function, optimization, architecture

4/ Focus on stochastic optimization algorithms, convergence proof of SGD

5/ Convolutional neural networks (CNNs): filters, layers, architectures

6/ Techniques: backpropagation, regularization, hyperparameters

7/ Networks for sequences: RNN, LSTM, Attention, Transformer

8/ Generative networks (GAN, VAE)

9/ Programming environments for neural networks: TensorFlow, Keras, PyTorch, and hands-on work with the examples covered in class

10/ Stable Diffusion, LLMs

11/ Ethical and alignment perspectives

Pré-requis obligatoire :

Python, Algèbre, Probabilités, Analyse numérique