Deep Learning
Objectives
- 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.
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
N/A
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.
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
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)