Aprendizagem Profunda
Objetivos
Present the area of deep learning. Discuss different architectures for addressing problems with images, time series, simple observations. Be able to implement and train a neural network in Keras.
Caracterização geral
Código
200180
Créditos
3.5
Professor responsável
Mauro Castelli
Horas
Semanais - A disponibilizar brevemente
Totais - A disponibilizar brevemente
Idioma de ensino
Português. No caso de existirem alunos de Erasmus, as aulas serão leccionadas em Inglês
Pré-requisitos
NA
Bibliografia
Deep Learning. Ian Goodfellow, Yoshua Bengio, Aaron Courville. MIT Press, 2016.
Método de ensino
Theoretical and practical classes.
Método de avaliação
First epoch: deep learning project.
Second epoch: deep learning project.
More details will be provided during the course.
Conteúdo
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 and 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).
Cursos
Cursos onde a unidade curricular é leccionada: