Knowledge Base Resources

• 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.

• 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.

• 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.

• 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.

• 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.

• Handwritten Digits PyTorch

This tutorial is essentially the "hello world" of image recognition and feed-forward neural network (using PyTorch). Using the MNIST database (filled within images of handwritten digits), the tutorial will instruct how to build a feed-forward neural network that can recognize handwritten digits. A solid understanding of feed-forward and back-propagation is recommended.

• Neocortex Documentation

Neocortex is a new supercomputing cluster at the Pittsburgh Supercomputing Center (PSC) that features groundbreaking AI hardware from Cerebras Systems.

• Framework to help in scaling Machine Learning/Deep Learning/AI/NLP Models to Web Application level

This framework will help in scaling Machine Learning/Deep Learning/Artificial Intelligence/Natural Language Processing Models to Web Application level almost without any time.

• Recording of CI ACCESS Talk
• Flyer about ACCESS
• Slides from CI ACCESS Talk

Materials from the SAIL meeting (https://aiinstitutes.org/2023/06/21/sail-2023-summit-for-ai-leadership/). A space where AI researchers can learn about using ACCESS resources for AI applications and research.

• 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)

• Fairness and Machine Learning

The "Fairness and Machine Learning" book offers a rigorous exploration of fairness in ML and is suitable for researchers, practitioners, and anyone interested in understanding the complexities and implications of fairness in machine learning.

• 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.

• An Introduction to Julia
• The Julia Computing Language

The Julia Programming Language is one of the fastest growing software languages for AI/ML development. It writes in manner that's similar to Python while being nearly as fast as C++, while being open source, and reproducible across platforms and environments. The following link provide an introduction to using Julia including the basic syntax, data structures, key functions, and a few key packages.

• Python Tools for Data Science

Python has become a very popular programming language and software ecosystem for work in Data Science, integrating support for data access, data processing, modeling, machine learning, and visualization. In this webinar, we will describe some of the key Python packages that have been developed to support that work, and highlight some of their capabilities. This webinar will also serve as an introduction and overview of topics addressed in two Cornell Virtual Workshop tutorials, available at https://cvw.cac.cornell.edu/pydatasci1 and https://cvw.cac.cornell.edu/pydatasci2

• Docs

MediaPipe is Google's open-source framework for building multimodal (e.g., video, audio, etc.) machine learning pipelines. It is highly efficient and versatile, making it perfect for tasks like gesture recognition. This is a tutorial on how to make a custom model for gesture recognition tasks based on the Google MediaPipe API. This tutorial is specifically for video-playback, though could be generalized to image and live-video feed recognition.

• https://ieeexplore.ieee.org/document/910572

A tutorial paper that presents a generic message-passing algorithm, the sum-product algorithm, that operates in a factor graph. Following a single, simple computational rule, the sum-product algorithm computes either exactly or approximately various marginal functions derived from the global function. A wide variety of algorithms developed in artificial intelligence, signal processing, and digital communications can be derived as specific instances of the sum-product algorithm, including the forward/backward algorithm, the Viterbi algorithm, the iterative "turbo" decoding algorithm, Pearl's (1988) belief propagation algorithm for Bayesian networks, the Kalman filter, and certain fast Fourier transform (FFT) algorithms

• 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.

• 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.

• Building ML models for everyone: understanding fairness in machine learning

This article discusses the importance of fairness in machine learning and provides insights into how Google approaches fairness in their ML models. The article covers several key topics: Introduction to fairness in ML: It provides an overview of why fairness is essential in machine learning systems, the potential biases that can arise, and the impact of biased models on different communities. Defining fairness: The article discusses various definitions of fairness, including individual fairness, group fairness, and disparate impact. It explains the challenges in achieving fairness due to trade-offs and the need for thoughtful considerations. Addressing bias in training data: It explores how biases can be present in training data and offers strategies to identify and mitigate these biases. Techniques like data preprocessing, data augmentation, and synthetic data generation are discussed. Fairness in ML algorithms: The article examines the potential biases that can arise from different machine learning algorithms, such as classification and recommendation systems. It highlights the importance of evaluating and monitoring models for fairness throughout their lifecycle. Fairness tools and resources: It showcases various tools and resources available to practitioners and developers to help measure, understand, and mitigate bias in machine learning models. Google's TensorFlow Extended (TFX) and What-If Tool are mentioned as examples. Google's approach to fairness: The article highlights Google's commitment to fairness and the steps they take to address fairness challenges in their ML models. It mentions the use of fairness indicators, ongoing research, and partnerships to advance fairness in AI. Overall, the article provides a comprehensive overview of fairness in machine learning and offers insights into Google's approach to building fair ML models.

• 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.

• 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.

• 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.

• 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.

• https://gaussianbp.github.io/

This website is an interactive introduction to Gaussian Belief Propagation (GBP). A probabilistic inference algorithm that operates by passing messages between the nodes of arbitrarily structured factor graphs. A special case of loopy belief propagation, GBP updates rely only on local information and will converge independently of the message schedule. The key argument is that, given recent trends in computing hardware, GBP has the right computational properties to act as a scalable distributed probabilistic inference framework for future machine learning systems.

• 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.