At a glance
Nicoletta Noceti - UniGe | MaLGa & DIBRIS - email@example.com
Jun 28 2021 , Jul 2 2021
either Teams or Via Dodecaneso 35, Genova, Italy
Application deadline: May 16
Notification of acceptance: June 1
Deep Learning is a branch of Machine Learning that has recently achieved astonishing results in a number of different domains.
This course will provide a hands-on introduction to Deep Learning, starting from its foundations and discussing the various types of deep architectures and tools currently available.
The theoretical classes will be accompanied by work in lab, which will constitute an integral part of the course, giving the possibility of practicing deep learning with examples from real-world applications, with particular focus on visual data.
Besides well established approaches, the course will also highlight current trends, open problems and potential future lines of research
The course is linked to the Computer Vision Crash Course. The two courses are self-contained and can be taken independently, but students interested in both will be provided extra information to better relate the two subjects.
Machine Learning reprise
Introduction to deep learning
From single layer perceptron to deep neural networks
Convolutional neural networks
Dealing with time data
Recurrent Neural Networks and Long-Short Term Memory
Generative and adversarial learning
Open issues, challenges and perspectives
Labs activities in Python using Keras and Tensorflow
Once accepted, each candidate has to follow the instructions in the acceptance email and proceed with the payment. The registration fee is non-refundable.
students and postdocs: waived
professionals: EUR 150
UniGe students and IIT affiliates: no fee
Vito Paolo Pastore - UniGe | MaLGa & DIBRIS - Vito.Paolo.Pastore@edu.unige.it
Modiana Pasquinelli - UniGe | MaLGa & DIBRIS - firstname.lastname@example.org
Goodfellow, Y. Bengio and A. Courville, Deep Learning book, MIT Press, 2016
Francois Chollet. Deep Learning with Python, Manning Pub., 2017.
Slides, notebooks, and a list of bibliographical references and additional material will be provided to attendants. All the course material is in English.