This curricular unit will provide the student with the skills to:
Understand:
- The foundations of deep learning
- Fundamentals of deep network computing
Optimization algorithms, activation functions, objective functions
- Different deep network architectures and their usefulness:
Dense, convolution, recurrent, generative models.
- Training and regularization of deep networks
- The importance of data characteristics and of training, validation and test sets
Be able to:
- Select appropriate models and loss functions for different problems.
- Use modern libraries for deep learning.
- Implement deep networks, optimize their hyper-parameters and train them.
- Evaluate the training of the models and the quality of the results.
Know:
- Types of problems solved with deep networks
- Architectures and regularization of deep networks
- Model selection methods and hyper-parameters
1. Introduction: fundamentals of deep learning, nonlinear transformations and overfitting.
2. Artificial neural networks, backpropagation. and deep feedforward networks.
3. Implementation and training of deep neural networks
4. Optimization and regularization of feedforward networks. Training, testing and cross validation.
5. Convolution networks, theory and practice
6. Unsupervised deep learning with autoencoders
7. Representation and transfer learning
8. Generative models
9. Recurrent networks and problems with sequential data
10. Reinforcement learning
11. Structured probabilistic models
12. Practical aspects of deep network selection, application and optimization
13. Data and model visualization.
14. Open problems in deep learning
Main textbook, mandatory:
Goodfellow, Ian, Bengio, Yoshua, Courville, Aaron: Deep Learning , MIT Press, 2016
Complementary reading:
Skansi, Sandro: Introduction to Deep Learning: From Logical Calculus to Artificial Intelligence , Springer, 2018
Géron, Aurélien: Hands-on machine learning with Scikit-Learn and TensorFlow: concepts, tools, and techniques to build intelligent systems O''''Reilly Media, Inc, 2017
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| Hours per credit | 28 | ||
| Hours per week | Weeks | Hours | |
| Aulas práticas e laboratoriais | 24.0 | ||
| Aulas teóricas | 24.0 | ||
| Avaliação | 8.0 | ||
| Self study | 60.0 | ||
| Project | 52.0 | ||
| Total hours | 168 | ||
| ECTS | 6.0 | ||