Deep Learning with Applications

The course presents Deep Learning from a theoretical and practical point of view, introduces the basic elements of learning (non-linear models, minimization techniques, cross-validation and hyper-parameter tuning) and focuses on supervised, unsupervised and reinforcement learning models

At the end of the course the student will be able to:

- illustrate deep learning models in the learning context
supervised, unsupervised and for reinforcement.

- identify deep learning models suitable for the resolution of
problems in physics and beyond.

- use software and libraries for the development of deep learning models.