• 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.

• ACESS HPC Workshop Series
• MPI Workshop
• OpenMP Workshop
• GPU Programming Using OpenACC
• Summer Boot Camp
• Big Data and Machine Learning

Monthly workshops sponsored by ACCESS on a variety of HPC topics organized by Pittsburgh Supercomputing Center (PSC). Each workshop will be telecast to multiple satellite sites and workshop materials are archived.

• 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.

• 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.

• 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.

• https://www.udacity.com/blog/2021/01/best-r-packages-for-data-science.html

This Udacity article listed the most frequently used R packages for data science and statistics. For each package, the article provided the link to its official documentation. It will be a great start point if you want to start your data science journey in R.

• Introduction to LSTMs

This reading will explain what a long short-term memory neural network is. LSTMs are a type of neural networks that rely on both past and present data to make decisions about future data. It relies on loops back to previous data to make such decisions. This makes LSTMs very good for predicting 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.

• Flexible and Robust Machine Learning Using mlr3 in R

The free online book for the mlr3 machine learning framework for R. Gives a comprehensive overview of the package and ecosystem, suitable from beginners to experts. You'll learn how to build and evaluate machine learning models, build complex machine learning pipelines, tune their performance automatically, and explain how machine learning models arrive at their predictions.

• 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://mc-stan.org/users/documentation/
• https://vasishth.github.io/bayescogsci/book/ch-introstan.html
• https://pystan.readthedocs.io/en/latest/

The Stan language is used to specify a (Bayesian) statistical model with an imperative program calculating the log probability density function. Here are some useful links to start your exploration of this statistical programming language, and a Python interface to Stan.

• 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.

• 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.

• https://ermongroup.github.io/cs228-notes/

This website summarizes the notes of Stanford's introductory course on probabilistic graphical models. It starts from the very basics and concludes by explaining from first principles the variational auto-encoder, an important probabilistic model that is also one of the most influential recent results in deep learning.

• Find Online Master's in Business Analytics

A degree in business analytics looks different in today’s world than it did a decade ago. In its most current application, business analytics uses modern data science and capabilities in machine learning (ML). The magic comes into play when these are leveraged for strategic planning.

• 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.

• Cursor - AI code editor

**Cursor: The AI-Powered Code Editor** Cursor is a cutting-edge, AI-first code editor designed to revolutionize the way developers write, debug, and understand code. Built upon the premise of pair-programming with artificial intelligence, Cursor harnesses the capabilities of advanced AI models to offer real-time coding assistance, bug detection, and code generation. **How Cursor Benefits High-Performance Computing (HPC) Work:** 1. **Efficient Code Development:** With AI-assisted code generation, researchers and developers in the HPC realm can quickly write optimized code for simulations, data processing, or modeling tasks, reducing the time to deployment. 2. **Debugging Assistance:** Handling complex datasets and simulations often lead to intricate bugs. Cursor's capability to automatically investigate errors and determine root causes can save crucial time in the HPC workflow. 3. **Tailored Code Suggestions:** Cursor's AI provides context-specific code suggestions by understanding the entire codebase. For HPC applications where performance is paramount, this means receiving recommendations that align with optimization goals. 4. **Improved Code Quality:** With AI-driven bug scanning and linter checks, Cursor ensures that HPC codes are not only fast but also robust and free of common errors. 5. **Easy Integration:** Being a fork of VSCode, Cursor allows seamless migration, ensuring that developers working in HPC can swiftly integrate their existing VSCode setups and extensions. In essence, for HPC tasks that demand speed, precision, and robustness, Cursor acts as an invaluable co-pilot, guiding developers towards efficient and optimized coding solutions. It is free if you provide your own OPEN AI API KEY.

• 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.

• Harnessing the Power of Cloud and Machine Learning for Climate and Ocean Advances
• Github for Outputs of Presentation

Documentation and presentation on how to use machine learning and deep learning framework using TensorFlow, Keras and sci-kit learn for Climate and Ocean Advances

• https://lectures.scientific-python.org/

Comprehensive tutorials and lecture notes covering various aspects of scientific computing using Python and Scipy.

• 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.

• 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.

• 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.

• Document Tutorial

This tutorial will teach step-by-step how to create an image classification model using Core ML in XCode and integrate it into an iOS app that will use the user's iPhone camera to scan objects and predict based on the image classification model.

• 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.