Deep Learning
Objectives
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.
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
NA
Bibliography
Deep Learning. Ian Goodfellow, Yoshua Bengio, Aaron Courville. MIT Press, 2016.
Teaching method
Theoretical and practical classes.
Evaluation method
First epoch: deep learning project.
Second epoch: deep learning project.
More details will be provided during the course.
Subject matter
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).