These course will be online. Details on the platform and schedule will be published soon.
13-17 July 2020, 9:00-13:00
Via Dodecaneso 35, room TBA
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 (with Python using Keras and Tensorflow), 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 and temporal data. Besides well established approaches, the course will also highlight current trends, open problems and potential future lines of research.
Fill this Form to apply to the Deep Learning: a hands-on introduction course
Mon, Jul 13
Machine Learning reprise + Introduction to deep learning: From single layer perceptron to deep neural networks
Tue, Jul 14
Convolutional neural networks Lab 2
Wed, Jul 15
Dealing with time data: Recurrent Neural Networks and Long-Short Term Memory
RNN + LSTM
Thu, Jul 16
Generative Adversarial Networks
Fri, Jul 17
Examples of applications to real world problems, open issues
Slides, notebooks, and a list of bibliographical references and additional material will be provided to attendants. All the course material is in English.
Goodfellow, Y. Bengio and A. Courville, Deep Learning book, MIT Press, 2016.
Francois Chollet. Deep Learning with Python, Manning Pub., 2017.