Clustering basics
In this lesson, we'll learn some of the key concepts behind clustering.
Teaching resources
Public tutorials and practical references for programming, NLP, applied machine learning, and research computing.
33 standalone lessons, technical references, and course-adjacent guides.
In this lesson, we'll learn some of the key concepts behind clustering.
In this lesson, we'll learn about distance and similarity metrics.
This lesson introduces you to the basics of Docker.
In this lesson, you'll learn how to use to represent words and documents with vectors of features.
This tutorial introduces the course workflow for using Forgejo repositories created through Class Maestro.
This lesson introduces you to the basics of the Git version control system.
This tutorial introduces you to using GitHub.
This lesson introduces tokenization as the step where language models turn written text into token IDs. It includes a short concrete example using the tokenizer associated with gpt-oss-20b, without loading the model weights.
This tutorial provides instructions for installing Docker on Ubuntu. While the steps outlined here may work for other versions of Ubuntu (and some non-Ubuntu Linux distributions), these instructions were tested against Ubuntu 24.04 LTS.
This tutorial provides instructions for installing the latest Ubuntu LTS (Long Term Support) version using VirtualBox.
This tutorial provides instructions for installing Visual Studio Code on Ubuntu. While the steps outlined here may work for other versions of Ubuntu (and some non-Ubuntu Linux distributions), these instructions were tested against Ubuntu 24.04 LTS.
This lesson introduces you to the concept of containerization.
In this tutorial, we're going to step through training a naïve Bayes classifier with n-gram features using a popular machine learning library in Python called scikit-learn. This library provides implementations and an easy-to-use interface for many supervised and...
In this lesson, we'll look at a definition of token and see some examples in English.
This is a brief look at tokenization and part of speech assignment in Japanese.
In this lesson, we'll learn about estimating the probability of n-grams and see how we can approximate the probability of a document using n-gram probabilities.
This tutorial provides instructions for launching an instance (virtual server) on Jetstream 2 using the Exosphere user interface.
This tutorial introduces you to the basics of the Linux command line.
youtube: https://youtu.be/wAstsGTgWw
In this lesson, we'll learn about n-grams and see how they can be used as general features to represent words and documents.
This lesson provides an introduction to named entities (NEs).
This lesson provides an introduction to parts of speech.
Ambiguity and uncertainty abound in natural language. In this lesson, we'll learn the basics of probability which will help us to quantify that uncertainty.
Suppose you ask a model about a paper you've just read. It gives you a confident, detailed answer. Has it actually read the paper? What changes if you supply the text yourself?
In this lesson, you'll be introduced to regular expressions (regexes) and practice using this pattern language to identify spans of text.
How might we represent a sequence while keeping track of what we've already seen? A recurrent neural network (RNN) updates a hidden state as it moves through the sequence. We'll follow those updates, connect them to PyTorch, and then look at gated RNNs and encoder-decoder...
This tutorial introduces SSH keys for Git-based coursework.
In this lesson, we'll learn a little about the connection between regular expessions and state machines. You'll explore this topic in much more depth in LING 538.
This lesson provides an introduction to text normalization.
In earlier lessons, we introduced the idea of tokens and discussed some of their attributes. We devised some informal descriptions of the clues that signal token boundaries (ex. whitespace, contractions, etc.). In this lesson, we'll how to use regexes to tokenize...
Suppose we're translating a story. The information we need for the next word may be somewhere in the source text, rather than in the last word we translated. How can a model revisit that information and decide what to use?
This tutorial provides a crash course in using the University of Arizona's High Performance Computing (HPC) clusters.
In this lesson, we'll learn about the components of vectors, as well as some basic vector operations.
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