Course Material

1. PyTorch Introduction (12.10.2021): Slides
2. Tensors (19.10.2021): Presentation, exercise 1, exercise 1 solution
3.  Data Encoding and Embeddings (26.10.2021): Presentation, exercise_2, exercise 2 solution
4. Building neural modules (02.11.2021): Presentation, exercise 3, exercise 3 solution
5. Training by gradient descent (09.11.2021): Presentation, exercise 4, exercise 4 solution
6. Training by gradient descent (continued) (16.11.2021): Exercise 5
7. Training by gradient descent (continued) (23.11.2021): Exercise 6, exercise 6 solution
8. Training by gradient descent (continued)/evaluation (30.11.2021): Exercise 7, exercise 7 solution
9. Cancelled
10. RNNs/LSTMs (14.12.2021): Exercise 8, exercise 8 solution
11. Pretrained embeddings (21.12.2021): Screencast, exercise 9, exercise 9 solution
12. Character model (11.01.2022): Exercise 10, exercise 10 solution
13. Convolutional Neural Networks (18.01.2022): Exercise 11, exercise 11 solution
14. Multi-task learning (25.01.2022): Exercise 12, exercise 12 solution
15. Attention (01.02.2022): Exercise 13, exercise 13 solution

Homework

Practical:

1. Homework 1 (due 02.11.21), solution
2. Homework 2 (due 14.12.21)
3. Homework 3 (due 01.02.22)

Theoretical:

1. Homework 1 (due 08.11.21)
2. Homework 2 (due 06.12.21)