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“Perplexity in t-SNE ML” is a study on T-distributed stochastic Neighbor embedding. It is uses on Non-linear multi-dimensional data representation in 2D to allow collaboration between designer and algorithm. A designed facade typology data set is fed to the machine to develop their own understanding of the image-based cultural representation. The play with size, resolution, perplexity, and iteration values throws different outputs to be voxelized as facade assemblies.

Made with Python and Processing using machine learning workflows.

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Type:               Workshop
Institution:    SoomeenHahm Design Platform
Instructor:     Sanghyun Suh
Research:      Machine Learning, t-SNE, Python,
                         Processing, 3D Voxelization.

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Automated Facade Iterations:
1. --size 39 --res 256--per 50 --iter 5000
2. --size 39 --res 256--per 150 --iter 25000
3. --size 32 --res 256--per 150 --iter 25000
4. --size 19 --res 256--per 150 --iter 5000
5. --size 21 --res 256--per 150 --iter 25000
6. --size 39 --res 256--per 150 --iter 5000
7. --size 39 --res 256--per 50 --iter 5000
8. --size 42 --res 128--per 100 --iter 5000
9. --size 30 --res 128--per 100 --iter 5000

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