This collection of notebooks accompanies the tutorial at the ALS User Meeting 2023.
The tutorial has been prepared by Tanny Chavez, Tibbers Hao, Alex Hexemer, Wiebke Koepp, Dylan McReynolds, Eric Roberts, Zhuowen Zhao, and Petrus Zwart.
This tutorial has been designed to be compatible with Google Colab, a free, cloud-based Jupyter notebook environment equipped with commonly used machine learning tools.
Get started with Colab:
The tutorial consists of the following hands-on sessions:
- Data pre-processing by Tibbers Hao:
- Dimensionality reduction by Eric Roberts:
- Fitting and Tensors by Petrus Zwart:
- Dataloaders by Zhuowen Zhao:
- Autoencoders by Wiebke Koepp:
- Convolutional Neural Networks by Tanny Chavez:
- chatGPT for ML Alex Hexemer:
MLExchange Copyright (c) 2023, The Regents of the University of California, through Lawrence Berkeley National Laboratory (subject to receipt of any required approvals from the U.S. Dept. of Energy). All rights reserved.
If you have questions about your rights to use or distribute this software, please contact Berkeley Lab's Intellectual Property Office at [email protected].
NOTICE. This Software was developed under funding from the U.S. Department of Energy and the U.S. Government consequently retains certain rights. As such, the U.S. Government has been granted for itself and others acting on its behalf a paid-up, nonexclusive, irrevocable, worldwide license in the Software to reproduce, distribute copies to the public, prepare derivative works, and perform publicly and display publicly, and to permit others to do so.