Deep Learning

Aim of the course is to present a complete overview of modern Deep Neural Networks. As a subfield of Machine Learning, the course also covers some elementary concepts such as un/supervised learning, metrics, training as loss optimization, and subsequently presents the most important recent Deep Learning technologies, with emphasis on popular architectures for analyzing text, images and other modalities. The lectures are accompanied with Python code and in order to give a hands-on feel of the subject.

Although the course Machine Learning is not technically a prerequisite, this course is a continuation and the previous course contents will be considered taught material.

You can find more info in the course's eClass page (in Greek).