Knowledge Base Resources

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.

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.

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.

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.

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.

Introduction seminar for new reactive python notebook from marimo ambassador.

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

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.

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

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.

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.

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.

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.

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.

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.

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.

**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.

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.

Humans cannot always be treated as oracles for collaborative sensing. Robots thus need to maintain beliefs over unknown world states when receiving semantic data from humans, as well as account for possible discrepancies between human-provided data and these beliefs. To this end, this paper introduces the problem of semantic data association (SDA) in relation to conventional data association problems for sensor fusion. It then, develops a novel probabilistic semantic data association (PSDA) algorithm to rigorously address SDA in general settings. Simulations of a multi-object search task show that PSDA enables robust collaborative state estimation under a wide range of conditions.

pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language. It lets you store data in easy to manage and display data frames, with column names and datatypes.

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.

tutorial on introduction to making a AI Chat assistant using GenAI API