Aprendizagem Profunda


- Understand the design principles of neural networks;

- Understand the concept of activation function;

- Understand the backpropagation algorithm for training a neural network;

- Being able to build a neural network to solve classification tasks;

- Being able to use Keras or similar libraries to build a Neural Network;

- Understand the convolution operator and the idea behind convolutional neural network;

- Understand the main principles of recurrent neural network; 

- Understand LSTM and how they can be applied to counteract vanishing gradient problem.

- Being able to apply one of the deep model presented to solve classification or regression tasks.

Caracterização geral





Professor responsável

Mauro Castelli


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




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.


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)


Cursos onde a unidade curricular é leccionada: