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/2013/05/episode-27-artificial-intelligence.html
- Episode 28: Applied Artificial Intelligence
- https://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 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
- Patrick’s Choice: “Flour, Water, Salt, Yeast” by Ken Forkish
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
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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