Deep Learning in Digital Media

Scientific innovations in the intersection of visual data, analytics, and artificial intelligence are powering an historical shift to “digital economy”, with a disruptive impact on emerging trends and markets ranging from entertainment, communications and autonomous driving to health and smart cities. In this course, we cover theoretical and practical concepts of deep learning applications in the above market sectors with topics including overview of neural networks and deep convolutional neural networks, in-depth coverage of activation, cost functions, optimization, training, back propagation and regularization. Commonly used architectures for video related applications are studied in detail with emphasis on networks for object detection, recurrent neural networks, long short term memory networks, generative adversarial networks, autoencoders and transformers.
In summary, a good overview of an exciting field and a look at future research trends and market opportunities.

Instructor Panos Nasiopoulos

3 credits

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