Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features.
Architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) or the Transformer based on Attention mechanisms have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
Learning Paradigms
Instructor: Xavier Giró-i-Nieto
Architectures
Instructor: Xavier Giró-i-Nieto
Training
Instructor: Xavier Giró-i-Nieto
Architectures
Instructor: Xavier Giró-i-Nieto
Architectures
Instructor: Xavier Giró-i-Nieto
Training
Instructor: Javier Ruiz Hidalgo
Architectures
Instructor: Veronica Vilaplana
Architectures
Instructor: Xavier Giró-i-Nieto
Learning Paradigms
Instructor: Xavier Giró-i-Nieto
Generative Models
Instructor: Xavier Giró-i-Nieto
Architectures
Instructor: Xavier Giró-i-Nieto & Marta R. Costa-Jussà
Architectures
Instructor: Xavier Giró-i-Nieto
Multimodal Learning
Instructor: Xavier Giró-i-Nieto