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
  • 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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