145: Unsupervised Machine Learning

Today we discuss adventures, books, tools, and art discoveries before diving into unsupervised machine learning in this duo episode!
00:00:22 Introductions
00:01:28 Email & inbox organization is very important
00:07:28 The Douglas-Peucker algorithm
00:11:48 Starter project selection
00:17:01 Tic-Tac-Toe
00:21:41 Artemis 1
00:26:25 Space slingshots
00:29:47 Flex Seal tape
00:32:38 The Meditations
00:37:58 Flour, Water, Salt, Yeast
00:40:55 Pythagorea
00:46:13 Google Keep
00:48:05 Visual-IF
00:50:49 Data insights
01:03:07 Self-supervised learning
01:10:26 A practical example of clustering
01:15:10 Word embedding
01:24:02 Farewells
Want to learn more? Check out these previous episodes:
Episode 27: Artificial Intelligence Theory
https://www.programmingthrowdown.com/episodes/145-unsupervised-machine-learning
Episode 28: Applied Artificial Intelligence
https://www.programmingthrowdown.com/episodes/145-unsupervised-machine-learning
Episode 109: Digital Marketing with Kevin Urrutia
https://www.programmingthrowdown.com/episodes/145-unsupervised-machine-learning
Resources mentioned in this episode:
News/Links:
Simplify lines with the Douglas-Peucker Algorithm
https://ilya.puchka.me/douglas-peucker-algorithm/
How to pick a starter project
https://amir.rachum.com/blog/2022/08/07/starter-project/
Tic-Tac-Toe in a single call to printf()
https://github.com/carlini/printf-tac-toe
Artemis 1
https://www.nasa.gov/artemis-1/
Visual-IF
https://www.visual-if.com/
Book of the Show:
Jason’s Choice: “The Meditations” by Marcus Aurelius
https://amzn.to/3C3Kg7b
Patrick’s Choice: “Flour, Water, Salt, Yeast” by Ken Forkish
https://amzn.to/3CqFwKa
Tool of the Show:
Jason’s Choice: Pythagorea
Android: https://play.google.com/store/apps/details?id=com.hil_hk.pythagorea&hl=en&gl=US
iOS: https://apps.apple.com/us/app/pythagorea/id994864779
Patrick’s Choice: Google Keep
https://keep.google.com/
References:
Clustering: https://en.wikipedia.org/wiki/Cluster_analysis
Autoencoding: https://en.wikipedia.org/wiki/Autoencoder
Contrastive Learning: https://towardsdatascience.com/understanding-contrastive-learning-d5b19fd96607
Matrix Factorization: https://en.wikipedia.org/wiki/Matrix_factorization_(recommender_systems)
Stochastic factorization: https://link.medium.com/ytuaUAYBjtb
Deep Learning: https://en.wikipedia.org/wiki/Deep_learning
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