Today we discuss adventures, books, tools, and art discoveries before diving into unsupervised machine learning in this duo episode!00:00:22 Introductions00:01:28 Email & inbox organization is very important00:07:28 The Douglas-Peucker algorithm00:11:48 Starter project selection00:17:01 Tic-Tac-Toe 00:21:41 Artemis 100:26:25 Space slingshots00:29:47 Flex Seal tape00:32:38 The Meditations00:37:58 Flour, Water, Salt, Yeast00:40:55 Pythagorea00:46:13 Google Keep00:48:05 Visual-IF00:50:49 Data insights01:03:07 Self-supervised learning01:10:26 A practical example of clustering01:15:10 Word embedding01:24:02 FarewellsWant to learn more? Check out these previous episodes:
Episode 27: Artificial Intelligence Theoryhttps://www.programmingthrowdown.com/2013/05/episode-27-artificial-intelligence.html

Episode 28: Applied Artificial Intelligencehttps://www.programmingthrowdown.com/2013/06/episode-28-applied-artificial.html

Episode 109: Digital Marketing with Kevin Urrutia
https://www.programmingthrowdown.com/2021/03/episode-109-digital-marketing-with.html


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 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/3C3Kg7b

Patrick’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_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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