This course is a detailed introduction to deep-learning, with examples in the PyTorch framework:
- machine learning objectives and main challenges,
- tensor operations,
- large-scale optimization with automatic differentiation and stochastic gradient descent,
- deep-learning specific techniques (batchnorm, dropout, residual networks),
- image understanding,
- generative models,
- recurrent models and NLP.
- Professor: François Fleuret
