Deep Learning

Course description

Introduction to Deep Learning. Concepts and main networks. Use cases in classification, regression, transfert learning, dimension reduction, data generation.

Syllabus

  1. Introduction to deep learning and to development tools (Tensorflow with Keras and Google colab )
  2. Perceptrons and MultiLayer Perceptrons
  3. Convolutional Networks
  4. Autoencoders
  5. RNN, LSTM and GRU
  6. Attention mechanisms and transformers
  7. Transfert Learning
  8. Generative models - GAN, VAE and diffusion
  9. Graph neural networks