Deep Learning
Objectives
N/A
General characterization
Code
200180
Credits
3.5
Responsible teacher
Mauro Castelli
Hours
Weekly - Available soon
Total - Available soon
Teaching language
Portuguese. If there are Erasmus students, classes will be taught in English
Prerequisites
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
Back propagation
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)
Bibliography
Deep Learning. Ian Goodfellow, Yoshua Bengio, Aaron Courville. MIT Press, 2016.
Teaching method
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.
Evaluation method
English
Subject matter
Theoretical and practical classes.