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 Theoryhttps://www.programmingthrowdown.com/2013/05/episode-27-artificial-intelligence.htmlEpisode 28: Applied Artificial Intelligencehttps://www.programmingthrowdown.com/2013/06/episode-28-applied-artificial.htmlEpisode 109: Digital Marketing with Kevin Urrutiahttps://www.programmingthrowdown.com/2021/03/episode-109-digital-marketing-with.html Resources mentioned in this episode: News/Links:Simplify lines with the Douglas-Peucker Algorithmhttps://ilya.puchka.me/douglas-peucker-algorithm/ How to pick a starter projecthttps://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 1https://www.nasa.gov/artemis-1/Visual-IFhttps://www.visual-if.com/ Book of the Show:Jason’s Choice: “The Meditations” by Marcus Aureliushttps://amzn.to/3C3Kg7bPatrick’s Choice: “Flour, Water, Salt, Yeast” by Ken Forkishhttps://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_analysisAutoencoding: https://en.wikipedia.org/wiki/AutoencoderContrastive Learning: https://towardsdatascience.com/understanding-contrastive-learning-d5b19fd96607Matrix Factorization: https://en.wikipedia.org/wiki/Matrix_factorization_(recommender_systems)Stochastic factorization: https://link.medium.com/ytuaUAYBjtbDeep Learning: https://en.wikipedia.org/wiki/Deep_learning If you’ve enjoyed this episode, you can listen to more on Programming Throwdown’s website: https://www.programmingthrowdown.com/ Reach out to us via email: programmingthrowdown@gmail.com You can also follow Programming Throwdown on  Facebook | Apple Podcasts | Spotify | Player.FM  Join the discussion on our Discord Help support Programming Throwdown through our Patreon ★ Support this podcast on Patreon ★