https://github.com/kuleshov/teaching-material

Teaching materials for the machine learning and deep learning classes at Stanford and Cornell
https://github.com/kuleshov/teaching-material

Last synced: about 22 hours ago
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Teaching materials for the machine learning and deep learning classes at Stanford and Cornell

• Host: GitHub
• URL: https://github.com/kuleshov/teaching-material
• Owner: kuleshov
• Created: 2016-01-07T23:02:13.000Z (almost 9 years ago)
• Default Branch: master
• Last Pushed: 2020-09-03T19:30:36.000Z (about 4 years ago)
• Last Synced: 2024-10-30T03:40:16.933Z (15 days ago)
• Language: Jupyter Notebook
• Homepage:
• Size: 1.3 MB
• Stars: 1,102
• Watchers: 78
• Forks: 1,410
• Open Issues: 4
• Metadata Files:
  • Readme: README.md

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README

        
# Tutorials for Machine Learning Courses at Stanford and Cornell

Preparatory material for machine learning courses at Stanford at Cornell. Covers Python and Numpy. This has been used for:

* The probabilistic graphical models and the deep learning courses at Stanford.

* The applied machine learning course and deep generative models courses at Cornell.

## Material

This repo currently holds:

* A [tutorial](https://github.com/kuleshov/cs228-material/blob/master/tutorials/python/cs228-python-tutorial.ipynb) on basic Python/Numpy that is necesseary to get started with the above machine learning classes.

You may follow the iPython notebook on github, or clone and execute it locally.

The notebook is based on an [earlier version](http://cs231n.github.io/python-numpy-tutorial/) prepared by Justin Johnson.