https://github.com/hse-aml/bayesian-methods-for-ml

Materials for "Bayesian Methods for Machine Learning" Coursera MOOC
https://github.com/hse-aml/bayesian-methods-for-ml

Last synced: 14 days ago
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Materials for "Bayesian Methods for Machine Learning" Coursera MOOC

• Host: GitHub
• URL: https://github.com/hse-aml/bayesian-methods-for-ml
• Owner: hse-aml
• Created: 2017-12-01T10:21:02.000Z (almost 7 years ago)
• Default Branch: master
• Last Pushed: 2020-09-09T13:42:48.000Z (about 4 years ago)
• Last Synced: 2023-10-20T20:15:32.598Z (about 1 year ago)
• Language: Jupyter Notebook
• Homepage: https://www.coursera.org/learn/bayesian-methods-in-machine-learning
• Size: 1020 KB
• Stars: 126
• Watchers: 12
• Forks: 114
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

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README

        
# Bayesian Methods for Machine Learning course resources



https://www.coursera.org/learn/bayesian-methods-in-machine-learning

Wellcome to our GitHub repo for the course. You can clone the repository and solve the assignments locally, or run them on Google Colab.

## Running on Google Colab

Google has released its own flavour of Jupyter called Colab, which has free GPUs!

Here's how you can use it:

1. Open https://colab.research.google.com, click **Sign in** in the upper right corner, use your Google credentials to sign in.

2. Click **GITHUB** tab, paste https://github.com/hse-aml/bayesian-methods-for-ml and press Enter

3. Choose the notebook you want to open, e.g. `week2/em_assignment.ipynb`

4. Click **File -> Save a copy in Drive...** to save your progress in Google Drive

5. To use GPU click **Runtime -> Change runtime type** and select **GPU** in Hardware accelerator box

6. If you run many notebooks on Colab, they can continue to eat up memory, you can check GPU memory usage with `! nvidia-smi`.