Knowledge Base Resources

• Introduction to PyTorch for Deep Learning

PyTorch is a Python library that supports accelerated GPU processing for Machine Learning and Deep Learning. In this tutorial, I will teach the basics of PyTorch from scratch. I will then explore how to use it for some ML projects such as Neural Networks, Multi-layer perceptrons (MLPs), Sentiment analysis with RNN, and Image Classification with CNN.

• Landing Page
• Pytorch Quickstart
• Pytorch Basics
• Pytorch GPU Support
• Regression and Classification with Fully Connected Neural Networks
• High Dimensional Data
• Datasets and data loading
• Building the network
• Computer Vision and Convolutional Neural Networks

This workshop series introduces the essential concepts in deep learning and walks through the common steps in a deep learning workflow from data loading and preprocessing to training and model evaluation. Throughout the sessions, students participate in writing and executing simple deep learning programs using Pytorch – a popular Python library for developing, training, and deploying deep learning models.

• DeapSECURE lesson modules

DeapSECURE is a training program to infuse high-performance computational techniques into cybersecurity research and education. It is an NSF-funded project of the ODU School of Cybersecurity along with the Department of Electrical and Computer Engineering and the Information Technology Services at ODU. The DeapSECURE team has developed six non-degree training modules to expose cybersecurity students to advanced CI platforms and techniques rooted in big data, machine learning, neural networks, and high-performance programming. Techniques taught in DeapSECURE workshops are rather general and transferable to other areas including science, engineering, finance, linguistics, etc. All lesson materials are made available as open-source educational resources.

• Introduction to Python book

This documentation contains introductory material on Python Programming for Digital Humanities and Computational Research. This can be a go-to material for a beginner trying to learn Python programming and for anyone wanting a Python refresher.

• Tutorial: Localized RAG Chatbot with ACCESS HPC

This tutorial shows how to set up an open-source customizable RAG chatbot to answer questions about documents you can choose. It uses Indiana's Jetstream 2 HPC, but should work on any major ACCESS HPC.

• Supervised Machine Learning Readiness

Supervised Machine Learning Readiness is a self-paced, beginner-friendly program designed for Earth systems scientists to explore the core principles of supervised machine learning. This series uses a combination of step-by-step frameworks, exploratory widgets, and low-code exercises in Jupyter Notebooks, to explore the full cycle of machine learning model development. No programming experience is required. By the end of the series, you will be able to recognize when machine learning is an appropriate tool and critically evaluate machine learning in Earth systems science contexts. Access requires a free NSF Unidata eLearning account.

• Leveraging AI in Generative Assets and Environments for Play

In this presentation, I will explore the recent advancements in AI-driven production of 3D-generative assets and environments, particularly focusing on their application in creating immersive, playful experiences. Platforms such as ChatGPT, Suno, and Speechify have ushered in a new era of digital creativity, facilitating the development of environments that not only entertain but also serve educational purposes. This session will delve into how these technologies are integrated into academic settings, specifically through a case study of the English Department's Digital Media Lab, known as Tech/Tech, which opened in 2022.

• Open-Source LLM RAG Enhancement

This beginner-friendly guide introduces Retrieval-Augmented Generation (RAG), a technique to enhance Large Language Models (LLMs) by integrating external data sources. It covers the fundamentals of AI, LLMs, and RAG, providing step-by-step instructions, examples, and visual aids. The guide also discusses tools like Milvus, Faiss, and LangChain, offering a practical approach to building smarter AI systems.

• Landing Page
• Preparing data for LLM training
• Small Language Models: an introduction to autoregressive language modeling
• Attention is all you need
• Other LLM Topics

This workshop focuses on developing an understanding of the fundamentals of attention and the transformer architecture so that you can understand how LLMs work and use them in your own projects.

• Scikit-learn

Scikit-learn is free software machine learning library for Python. It has a variety of features you can use on data, from linear regression classifiers to xg-boost and random forests. It is very useful when you want to analyze small parts of data quickly.

• Automated Machine Learning: Methods, Systems, Challenges

The authoritative book on automated machine learning, which allows practitioners without ML expertise to develop and deploy state-of-the-art machine learning approaches. Describes the background of techniques used in detail, along with tools that are available for free.

• Powerpoint - Cursor and Termius benefits for HPC

These slides provide an introduction on how Termius and Cursor, two new and freemium apps that use AI to perform more efficient work, can be used for faster HPC research.

• Tutorial Link
• Airline Data Link

This google colab notebook tutorial demonstrates how to create and train an lstm model in pytorch to be used to predict time series data. An airline passenger dataset is used as an example.

• HPC-AI Resources for STEM and Non-STEM Researchers

This repository offers accessible resources and workshops on AI and high-performance computing (HPC), designed for both STEM and non-STEM majors. The materials are presented in simple language, requiring no prior technical background, making them suitable for a wide range of learners. The focus is on bridging the AI digital gap and enabling participants to harness the power of AI and HPC for research, innovation, and discovery.

• Awesome Jupyter Widgets List

A curated list of awesome Jupyter widget packages and projects for building interactive visualizations for Python code

• fast.ai Homepage

Fastai offers many tools to people working with machine learning and artifical intelligence including tutorials on PyTorch in addition to their own library built on PyTorch, news articles, and other resources to dive into this realm.

• Active Inference: The Free Energy Principle in Mind, Brain, and Behavior

This textbook is the first comprehensive treatment of active inference, an integrative perspective on brain, cognition, and behavior used across multiple disciplines including computational neurosciences, machine learning, artificial intelligence, and robotics. It was published in 2022 and it's open access at this time. The contents in this textbook should be educational to those who want to understand how the free energy principle is applied to the normative behavior of living organisms and who want to widen their knowledge of sequential decision making under uncertainty.

Computing Module: Introduces fundamental concepts and skills of Cyberinfrastructure (CI) and High-Performance Computing (HPC) to lower the barrier to becoming CI users in disaster management research. The module will cover the critical topics of CI and HPC with hands-on sessions. ​ Disaster Data Module: Introduces concepts of geospatial big data in disaster management. Students will learn how to access and process disaster data. ​ Geospatial Analytic Module: Introduces geospatial analytics skills to address real-world challenges in disaster management. The module will use the data introduced in the Disaster Data Module and cover various geospatial analytics topics such as geosimulation, spatial optimization, network analysis, terrain analysis, Geospatial Artificial Intelligence (GeoAI), social sensing, and CyberGIS.

• scikit learn tutorial

In the realm of Python-based machine learning, Scikit-Learn stands out as one of the most powerful and versatile tools available. This introductory post serves as a gateway to understanding Scikit-Learn through explanations of introductory ML concepts along with implementations examples in Python.

• Presentation slides
• GitHub Repository of Examples and Hands-on Exercises

This technology lab contains a set of sessions to help a new user start an AI project on the ACES cluster, a composable accelerator testbed at Texas A&M University. You will learn how to create and activate a virtual environment, manipulate and visualize data with Pandas and Matplotlib, use Scikit-learn for linear regression and classification applications, and use Pytorch to create and train a simple image classification model with deep neural networks (DNN).

• Numpy and Pandas for Data Analysis

In this tutorial, I present an overview with many examples of the use of Numpy and Pandas for data analysis. Beginners in the field of data analysis can find It incredibly helpful, and at the same time, anyone who already has experience in data analysis and needs a refresher can find value in it. I discuss the use of Numpy for analyzing 1D and 2D multidimensional data and an introduction on using Pandas to manipulate CSV files.

• MNIST NN Tutorial

This tutorial will give you an introduction to neural networks using the ever-famous MNIST handwritten digits database! Presented by Robin Hwang.

• Workshop Resources

4/5/25 - 4/6/25 The Duke IEEE Student Chapter is working with ACCESS to host a workshop on a introduction to supercomputing. All workshop resources are available on https://workshop.dukeieee.org/ Topics include: Here's a summarized list of topics for the event: Day 1: Saturday, April 5th Opening Remarks & ACCESS Overview (Including how to request compute usage with Jetstream 2) Tutorial 1: Introduction to Supercomputing Architecture, Linux, and Job Scheduling (SLURM) Tutorial 2: Containerized Large Language Model Inference and Finetuning Tutorial 3: Portable Code - Local Containers to HPC Scale Tutorial 4: ACCESS Pegasus - Serverless Data Processing Workflow in Jupyter Notebooks Networking & Hors D'oeuvres Day 2: Sunday, April 6th Tutorial 2: Deep Dive in AI Agents - "Building Superintelligence in 90 Minutes" by Harry Fazzone Tutorial 3: DASK - Python-based Distributed Computing Framework for HPC Tutorial 4: Basic Parallelism & MPI by Rebecca Hartman-Baker, PhD (NERSC) Closing Talk: Capt. Grace Hopper on Future Possibilities: Data, Hardware, Software, and People (Part One, 1982)

• JupyterLabIDE GitHub Repository

Iterative Programming takes place when you can explore your code and play with your objects and functions without needing to save, recompile, or leave your development environment. This has traditionally been achieved with a REPL or an interactive shell. The magic of Jupyter Notebooks is that the interactive shell is saved as a persistant document, so you don't have to flip back and forth between your code files and the shell in order to program iteratively. There are several editors and IDE's that are intended for notebook development, but JupyterLab is a natural choice because it is free and open source and most closely related to the Jupyter Notebooks/iPython projects. The chief motivation of this repository is to enable an IDE-like development environment through the use of extensions. There are also expositional notebooks to show off the usefulness of these features.

• InsideHPC HomePage

InsideHPC is an informational site offers videos, research papers, articles, and other resources focused on machine learning and quantum computing among other topics within high performance computing.