CSCI 167 - INTRO TO MACHINE LEARNING

Github repo for CSCI 167 at CSU Fresno.

SETUP

Virtual environment not included, but the version I've been using for this is Python 3.12.1 with these terminal commands (in Windows):

> python -m venv .venv
> .venv/Scripts/activate
> pip install numpy
> pip install matplotlib

And from there, you should be done! I also use vscode as the text editor for Jupyter notebooks.

Sorry for working on this in the middle of class, Dr. Ruby.

Name		Name	Last commit message	Last commit date
Latest commit History 22 Commits
.gitignore		.gitignore
1_1_BackgroundMathematics.ipynb		1_1_BackgroundMathematics.ipynb
2_1_Supervised_Learning.ipynb		2_1_Supervised_Learning.ipynb
3_1_Shallow_Networks_I.ipynb		3_1_Shallow_Networks_I.ipynb
3_2_Shallow_Networks_II.ipynb		3_2_Shallow_Networks_II.ipynb
3_3_Shallow_Network_Regions.ipynb		3_3_Shallow_Network_Regions.ipynb
3_4_Activation_Functions.ipynb		3_4_Activation_Functions.ipynb
6_1_Line_Search.ipynb		6_1_Line_Search.ipynb
6_2_Gradient_Descent.ipynb		6_2_Gradient_Descent.ipynb
6_3_Stochastic_Gradient_Descent.ipynb		6_3_Stochastic_Gradient_Descent.ipynb
6_4_Momentum.ipynb		6_4_Momentum.ipynb
6_5_Adam.ipynb		6_5_Adam.ipynb
7_1_Backpropagation_in_Toy_Model.ipynb		7_1_Backpropagation_in_Toy_Model.ipynb
7_2_Backpropagation.ipynb		7_2_Backpropagation.ipynb
8_1_MNIST_1D_Performance.ipynb		8_1_MNIST_1D_Performance.ipynb
README.md		README.md
mnist1d_data.pkl		mnist1d_data.pkl