Weekly - Available soon
Total - Available soon
Portuguese. If there are Erasmus students, classes will be taught in English
Introduction to deep learning
History and cognitive basis of neural computation.
The perceptron / multi-layer perceptron
The neural net as a universal approximator
Optimization by gradient descent
Overfitting and regularization (Dropout)
Convolutional Neural Networks (CNNs)
Training with shared parameters: the convlutional model
Recurrent Neural Networks (RNNs)
Exploding/vanishing gradients Long Short-Term Memory Units (LSTMs)
Deep Learning. Ian Goodfellow, Yoshua Bengio, Aaron Courville. MIT Press, 2016.
First epoch: deep learning project.
Second epoch: deep learning project.
The project is the same for the two epochs.
The student can defend the project in only one of the two epochs. If a student fails the first epoch he/she is responsible for proposing a new project that must be validated by the professor. The same applies to students that want to improve the grade.
Theoretical and practical classes.
Programs where the course is taught: