Our objective is to help prepare the future United States Department of Energy computer and computational science workforce. This includes:

• Potential or incoming undergraduate interns, part of e.g. SULI and CCI programs;
• High school students (possibly preparing for a Brookhaven National Laboratory HSRP program or related);
• Interested staff, postdocs and graduate students;
• The public in general!

We will cover the basic numerical and machine learning skills needed to succeed on computing projects, as well as link to curated external resources. It is intended for students and professionals at all levels, and assumes little-to-no knowledge of Python or machine learning concepts. Enjoy!

📕 Overview

[!tip] Use the links in the various README.md files in this repository (including this one) to access the content easily!

[!important] All of our tutorials are presented using Google Colaboratory. It's a simple way to run Jupyter Notebooks in the cloud, and requires zero local installations! Each module has links in the the respective README.md files which open the notebook in Google "Colab". From there, you can save the notebook to your own Google Drive. To do this from an open Google Colab instance, first ensure you're signed in (profile in the top right), then simply click "File" at the top left, then click "Save a copy in Drive", which will automatically open a new copy of the notebook, one which was saved to your personal Drive! That way you don't lose any progress.

Basic Python, data science, and machine learning

If you are new to these concepts, start here!

1. Python crash course [doc]
2. Introduction to NumPy, tabular data, and visualization [doc]
3. Introduction to machine learning (conceptual) & introduction to supervised machine learning [doc]
4. Intermediate supervised machine learning [doc]
5. Introduction to unsupervised learning [doc]

Machine learning today

🚀 Coming soon!

6. Neural networks from scratch [doc]
7. Introduction to PyTorch and deep learning libraries
8. Convolutional neural networks and practical GPU training
9. Introduction to transformer-based architectures

💲 Funding Acknowledgement

[!NOTE] This was originally created as a 2-week AI/ML bootcamp for the Rising STEM Scholars program.

The development of this content is partly supported by the Brookhaven National Laboratory Center for Computing Sciences Education and Support (CCSES), and by Brookhaven National Laboratory under Contract No. DE-SC0012704.

The Software resulted from work developed under a U.S. Government Contract No. DE-SC0012704 and are subject to the following terms: the U.S. Government is granted for itself and others acting on its behalf a paid-up, nonexclusive, irrevocable worldwide license in this computer software and data to reproduce, prepare derivative works, and perform publicly and display publicly.

THE SOFTWARE IS SUPPLIED "AS IS" WITHOUT WARRANTY OF ANY KIND. THE UNITED STATES, THE UNITED STATES DEPARTMENT OF ENERGY, AND THEIR EMPLOYEES: (1) DISCLAIM ANY WARRANTIES, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO ANY IMPLIED WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE OR NON-INFRINGEMENT, (2) DO NOT ASSUME ANY LEGAL LIABILITY OR RESPONSIBILITY FOR THE ACCURACY, COMPLETENESS, OR USEFULNESS OF THE SOFTWARE, (3) DO NOT REPRESENT THAT USE OF THE SOFTWARE WOULD NOT INFRINGE PRIVATELY OWNED RIGHTS, (4) DO NOT WARRANT THAT THE SOFTWARE WILL FUNCTION UNINTERRUPTED, THAT IT IS ERROR-FREE OR THAT ANY ERRORS WILL BE CORRECTED.

IN NO EVENT SHALL THE UNITED STATES, THE UNITED STATES DEPARTMENT OF ENERGY, OR THEIR EMPLOYEES BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, CONSEQUENTIAL, SPECIAL OR PUNITIVE DAMAGES OF ANY KIND OR NATURE RESULTING FROM EXERCISE OF THIS LICENSE AGREEMENT OR THE USE OF THE SOFTWARE.