Understanding Deep Learning Contents

UDL을 매주 공부해 볼 예정입니다.

simon J.D. Prince 의 책으로 공부를 진행하겠습니다.

UDL의 목차를 살펴봅시다.

contents

1.Introduction

2.Supervised learning

3. Shallow neural networks

4. Deep neural networks

5. Loss functions

6. Fitting models

7. Gradients and initialization

8. Measuring performance

9. Regularization

10. Convolutional networks

11. Residual networks

12. Transformers

13. Graph neural networks

14. Unsupervised learning

15. Generative Adversarial Networks

16. Normalizing flows

17. Variational autoencoders

18. Diffusion models

19. Reinforcement learning

20. Why does deep learning work?

21. Deep learning and ethics

A. Notion
B. Mathematics
C. Probability

UDL을 공부하는데 도움이 되는 사이트를 소개합니다.
출처
https://udlbook.github.io/udlbook/