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.

• 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.

• 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.

• 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.

• 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.

• Neural Networks in Julia using Flux.jl

Making a neural network has never been easier! The following link directs users to the Flux.jl package, the easiest way of programming a neural network using the Julia programming language. Julia is the fastest growing software language for AI/ML and this package provides a faster alternative to Python's TensorFlow and PyTorch with a 100% Julia native programming and GPU support.

• Active inference institute website

Active inference is an emerging study field in machine learning and computational neuroscience. This website in particular introduces "active inference institute", which has established a couple of years ago, and contains a wide variety of resources for understanding the theory of active inference and for participating a worldwide active inference community.

• A survey on datasets for fairness-aware machine learning

The research paper provides an overview of various datasets that have been used to study fairness in machine learning. It discusses the characteristics of these datasets, such as their size, diversity, and the fairness-related challenges they address. The paper also examines the different domains and applications covered by these datasets.

• Walkthrough Site
• Google Colab

A walkthrough (with a Google Colab link) on how to implement your own LSTM to observe time-dependent behavior.

• Data Imputation Methods for Climate Data and Mortality Data - Slices
• Github repository
• Data Imputation Methods for Climate Data and Mortality Data - Full Tutorial

This slices and videos introduced how to use K-Nearest-Neighbors method to impute climate data and how to use Bayesian Spatio-Temporal models in R-INLA to impute mortality data. The demos will be added soon.

• Intro to Machine Learning on HPC

This tutorial introduces machine learning on high performance computing (HPC) clusters. While it focuses on the HPC clusters at The University of Arizona, the content is generic enough that it can be used by students from other institutions.

• 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

• Beautiful Soup Docs

This package lets you easily scrape websites and extract information based on html tags and various other metadata found in the page. It can be useful for large-scale web analysis and other tasks requiring automated data gathering.

• Markov Decision Processes in Julia

The following link provides an easy method of implementing Markov Decision Processes (MDP) in the Julia computing language. MDPs are a class of algorithms designed to handle stochastic situations where the actor has some level of control. For example, used at a low level, MDPs can be used to control an inverted pendulum, but applied in higher level decision making the can also decide when to take evasive action in air traffic management. MDPs can also be extended to the partially observable domain to form the Partially Observable Markov Decision Process (POMDP). This link contains a wealth of information to show one can easily implement basic POMDP and MDP algorithms and apply well known online and offline solvers.