CPSC 370: Deep Learning

School: Chapman University
Term: Fall 2019
Website: Class homepage

Topics

0. Prerequisites
1. Linear Regression to Multi Layer Perceptrons
2. Universal Approximation Theorem
3. Training Neural Networks
4. Convolutional Neural Networks
5. Convolutional Neural Network Applications
6. Autoencoders
7. Recurrent Neural Networks
8. Attention
9. Generative Adversarial Networks
10. Neuroscience and Deep Learning
11. Reinforcement Learning Part 1
12. Reinforcement Learning Part 2

First day checklist

• [ ] Read the prerequisites notebook
• [ ] Run python ready_for_class.py
• [ ] Complete the test questions in here. These are due on the first day

To view the notebooks as slides run

jupyter nbconvert Notebooks/01\ Introduction.ipynb --to slides --post serve

Resources

Blogs

lilianweng.github.io
colah.github.io
Gradient

Other Courses

Machine Learning

• UBC

Deep Learning

• Stanford CS231
• CMU

Reinforcement Learning

• UCL
• Berkeley