| Lecturer: | I. Giulini |
| Period: |
Term 3 |
| ECTS: | 3 |
| Schedule: | 2 hours of lecture per week |
Outline
- Introduction to Deep Learning
- Forward and backward propagation and solvers
- Embeddings, matrix factorization, factorization machines and recommender systems
- Convolutional neural networks for image classification
- Network architectures for object detection and image segmentation
- Recurrent neural networks, Long Short-Term Memory (LSTM) units for learning based on sequences
- Learning for sequences to sequences, attention and memory
- Unsupervised deep learning and generative models
Bibliographie
- Goodfellow, I. and Bengio, Y., and Courville, A. (2016). Deep Learning. MIT Press.
- Chollet, F. and Allaire, J. J. (2018). Deep Learning with R. Manning Pub.
- Chollet, F. and Allaire, J. J. (2018). Deep Learning with Python. Manning Pub.
