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Warning, this live stream was a mess and is probably unwatchable! But if you choose to continue you'll see me attempt to build a Naive Bayes text classifier from scratch in the p5.js web editor! I start by explaining the core concepts behind Bayes' Theorem (multiple times) before diving into the code. You'll see my raw, unedited process of structuring the data, implementing the algorithm, debugging issues, and finally building a simple interactive sentiment analysis demo.
Code from live:
Polished code:
00:00:00 Choo choo!
00:05:35 Welcome & Announcements
00:10:49 Fall Plans
00:14:19 Technical Difficulties: Whiteboard Setup
00:23:20 Main Topic: Bayesian Text Classification
00:33:03 Whiteboard: Explaining Bayes' Theorem (Attempt 1)
00:44:02 Technical Difficulties: Muted Microphone
00:45:52 Whiteboard: Explaining Bayes' Theorem (Attempt 2)
01:24:54 Live Coding Begins
01:32:42 Re-recording the Introduction for Edited Video
01:40:13 Technical Difficulties: Camera Overheating
02:04:10 Coding Resumes: Building the Classifier
02:51:10 Explaining Laplacian (Additive) Smoothing
03:12:20 Normalizing the Final Probabilities
03:24:12 Coding Challenge Complete!
03:24:43 Reviewing a More Polished Version of the Code
03:30:35 Good bye!
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2025/09/15
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