Deep Learning Methods in Finance
Objetivos
Present the area of deep learning. Discuss different architectures and the guidelines for building a deep learning neural network. Be able to implement and train a neural network in Keras.
Caracterização geral
Código
400107
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.
The project is the same for the two epochs. The student can defend the project in 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.
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
Back propagation Overfitting and regularization (Dropout)
Convolutional Neural Networks (CNNs)
Training with shared parameters: the convlutional model Recurrent Neural Networks (RNNs)