This course provides an introduction to the field of deep learning, covering the main deep learning techniques from both a theoretical and practical point of view. Architectures that will be covered include Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Autoencoders, and Generative Adversarial Networks (GANs) The goal of the course is that of equipping students with the necessary skills to access the rapidly evolving field of deep learning. To this end lectures and labs will draw multiple links to both the Industry and state-of-the-art academic research, allowing the students to pursue a successful career in both directions. The practical elements of the course will be based on the PyTorch framework, a widely used open source Python framework for deep learning.

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