1. Access to software
2. Python examples (notebook)
3. pandas examples (archive)
4. Basics of Neural Networks
5. Intro to PyTorch tensors (notebook)
6. Chapter Six
7. Chapters Seven and Eight
8. Computer Vision History
9. word2vec algorithm
10. Chapter Nine
11. Hugging Face Transformers (notebook)
12. Chapter Ten
13. Chapters Eleven to Fourteen
14. Building LLMs
15. Running LLMs
16. Generating Images from Text
17. Reinforcement Learning Intro
18. More RL Examples

Syllabus

The syllabus for this course is here.

Textbook web page

The publisher's page for the textbook is here.

The first midterm exam is coming up on in class Friday, Oct. 17. Here is some information on how to prepare for the exam.

The final exam is coming up. Here is some information on how to prepare.

1. Cycling regression: due Wed., Oct. 1
2. Cats and Dogs: due Wed., Oct. 15
3. Gender Classifier: due Wed., Oct. 29
4. Color Diffusion: due Wed., Nov. 5