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5.41. Deep Learning (Mandatory)

5.41.1. Justification ↑ Back to top

This course covers modern deep learning fundamentals, including convolutional networks, transformer architectures, and training techniques for advanced AI systems.

5.41.2. Generales Goals ↑ Back to top

1. Implement deep neural networks using modern frameworks (PyTorch/TensorFlow)
2. Design architectures for specific domains (vision, language, etc.)
3. Analyze ethical limitations of deep models

5.41.3. Contribution to Outcomes ↑ Back to top

AG-C09) Design and Development of Solutions: Designs, implements, and evaluates solutions for complex computing problems. (Usage)

AG-C11) Use of Tools: Applies modern computing tools in problem solving. (Assessment)

5.41.4. Content ↑ Back to top

5.41.4.1. DL Foundations (15 hours) [Skills AG-C09,AG-C11] ↑ Back to top

Bibliography: (Goodfellow et al., 2016b)

Topics

1. Modern backpropagation
2. Regularization (Dropout, BatchNorm)
3. Advanced optimizers

Learning Outcomes

1. Implement DNNs from scratch [Assessment]
2. Diagnose training problems [Usage]

5.41.4.2. Advanced Architectures (25 hours) [Skills AG-C09,AG-C11] ↑ Back to top

Bibliography: (Vaswani et al., 2017; He et al., 2016)

Topics

1. ResNets and deep CNNs
2. Transformers and attention
3. Generative models (VAEs, GANs)

Learning Outcomes

1. Adapt architectures to specific domains [Assessment]
2. Train transformers on real datasets [Usage]

5.41.5. Bibliography ↑ Back to top

Goodfellow, I., Bengio, Y., and Courville, A. (2016b). Deep Learning. MIT Press.

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A., Kaiser, L., and Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30.

He, K., Zhang, X., Ren, S., and Sun, J. (2016). Deep residual learning for image recognition. CVPR, pages 770–778.