1. Access to software
2. Introduction to Python (Notebook)
3. Early history of neural networks
4. The deep learning era
5. Access to more servers
6. Introduction to Pandas (Notebook)
7. Cats and dogs (Notebook)
8. Advanced computer vision models
9. Understanding Word2Vec
10. Understanding Transformers
11. Hugging Face Transformers
12. Cartpole problem
13. Breakout example
14. AlphaGo and AlphaZero
15. How DALL-E 2 works
16. How Stable Diffusion works

1. Predicting Diabetes: due Wednesday, Sept. 27
2. Classifying road signs: due Wednesday, Oct. 4
3. Shakespeare does autocomplete: due Friday, Oct. 13
4. Classifying comments on github: due Wednesday, Oct. 18
5. Digits to words: due Monday, Oct. 30
6. Keras cartpole: due Friday, Nov. 10

The first midterm exam is coming up on Monday, Oct. 9. Here is information about what will be on the exam.

The second midterm exam is coming up on Wednesday, Nov. 9. Here is information about what will be on the exam.

The final exam is coming up on Monday, Nov. 20 from 11:30-2:00. Here is information about what will be on the final.

Syllabus

The syllabus for this course is here.

Textbook Notebooks

The author of our textbook has provided access to a set of companion notebooks. You can download these notebooks from the author's Github page.

Ferlitsch Github

Andrew Ferlitsch has posted a really extensive set of notes and code examples to Github.