Edinburgh JS talk - Text Wrangling and Machine Learning

Talk description
More organisations are looking to automate tasks and gain insights from large amounts of text. JavaScript is a powerful language for text wrangling and machine learning, allowing developers to quickly and easily manipulate text data. We will look at different approaches from rules-based parsing to neural nets.

Exploring what JavaScript frameworks, workflows and processes are available to build real world apps. This is a gentle introduction which should be interesting and useful to all JavaScript coders.

Video
Video of the talk: Starts at 46:30

Slides
The slides for the talk can be found in github repo slides.pdf

Code and examples
To view and play with the notebooks you will need to install the https://marketplace.visualstudio.com/items?itemName=donjayamanne.typescript-notebook plugin for Visual Code. Note: without Visual Code and the typescript-notebook plugin the notebook files .nnb will just be displayed as JSON data. It is also worth installing the Jupiter Notebooks plugin into Visual Code.

Once you have done the above then clone the git repo onto your local machine and do a npm install in the cloned directory Open the project in Visual Code and you should be able to play with the examples in the note books.

Prompt
The prompt tool demo is in a public repo on https://github.com/glennjones/prompt - It well worth looking at the Langchain JS which is the most heavily used tool of this type at the moment (Apr 2023) Its more advanced and has a lot of additional/useful features my prompt tool does not have.