At the end of the course, students will be able to use state-of-the-art tools to implement deep-learning solutions for real-world problems. They will be also able to understand the practical issues posed by advanced deep-learning techniques.
Prerequisiti
The course requires a basic knowledge of the main concepts of machine learning and artificial neural networks. It also requires students to be able to write simple scripts in the Python programming language.
Metodi didattici
The course is delivered in the form of practical exercises in which students reimplement and extend case studies presented by the teachers.
Verifica Apprendimento
To pass the exam, students must prepare reports describing the activities carried out in the lab. As an optional alternative, students attending the lab may replace the reports by answering to short quizzes.
Testi
Material prepared by the teachers and a selection of recommended papers.
Contenuti
The course begins with an introduction to the state-of-the-art frameworks for the development of systems based on deep learning models. Then, it presents the implementation of advanced deep learning techniques such as sequence-to-sequence architectures and generative models (variational autoencoders, generative adversarial networks, and diffusion models). The practical issues related to the implementation of these techniques are illustrated by their application to real-world problems taken from the domains of computer vision and audio processing.
