Internal ML course/practical demonstration intern to NERSC
- Session 1: Introduction, generalities on machine learning (pdf)
- Session 2: Validation, overfitting, regularization (pdf)
- Session 3: Random Forest, grid search (pdf)
- Session 4: Neural networks (pdf)
- Session 5: Convolutional neural networks (pdf)
Run the tutorial in a cloud computing provider (require Google login):
- Practice 1: Introduction and linear regression
- Practice 2: Validation, overfitting, regularization
- Practice 3: Random forests. Grid search.
- Practice 4: Neural networks.
- Practice 5: Convolutional Neural Networks and Regularizations.
- HACKATHON Data for hackathon.
You can also run this notebook on your own (Linux/Windows/Mac) computer. This is a bit snappier than running them online.
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Prerequisite: Python>=3.7. If you're not a python expert: 1a. Install Python via Anaconda. 1b. Use the Anaconda terminal to run the commands below. 1c. (Optional) Create & activate a new Python environment. If the installation (below) fails, try doing step 1c first.
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Install: Run these commands in the terminal (excluding the
$
sign):$ git clone https://github.com/nansencenter/nersc_ml_course.git
$ pip install -r nersc_ml_course/requirements.txt
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Launch the Jupyter notebooks:
$ jupyter-notebook
This will open up a page in your web browser that is a file navigator.
Enter the foldernersc_ml_course/notebooks
, and click on the tutorialnotebook_name.ipynb