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t-distributed Stochastic Neighbor Embedding (t-SNE) | Dimensionality Reduction Techniques (4/5)

DeepFindr 8,981 11 months ago
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To try everything Brilliant has to offer—free—for a full 30 days, visit https://brilliant.org/DeepFindr. The first 200 of you will get 20% off Brilliant’s annual premium subscription. (Video sponsered by Brilliant.org) ▬▬ Papers / Resources ▬▬▬ Colab Notebook: https://colab.research.google.com/drive/1n_kdyXsA60djl-nTSUxLQTZuKcxkMA83?usp=sharing Entropy: https://gregorygundersen.com/blog/2020/09/01/gaussian-entropy/ Attractive / Repulsive Forces Gradient: https://jmlr.org/papers/volume23/21-0055/21-0055.pdf t-SNE Parameters distill: https://distill.pub/2016/misread-tsne/ Other great resources: - By the t-SNE author: https://lvdmaaten.github.io/tsne/ - A good view on probability: https://siegel.work/blog/tSNE/ - CalTech tutorial: http://bebi103.caltech.edu.s3-website-us-east-1.amazonaws.com/2016/tutorials/aux8_tsne.html - Great visuals: https://newsletter.theaiedge.io/p/formulating-and-implementing-the - SNE vs T-SNE: https://www.linkedin.com/pulse/visualization-method-sne-vs-t-sne-implementation-using-tandia/ - t-SNE in raw numpy: https://nlml.github.io/in-raw-numpy/in-raw-numpy-t-sne - t-SNE in raw javascript: https://observablehq.com/@nstrayer/t-sne-explained-in-plain-javascript - Video by the t-SNE author: https://www.youtube.com/watch?v=MgawSHnYQGw&t=2604s&ab_channel=ComputerVisionFoundationVideos Image Sources: - Perplexity image: https://stats.stackexchange.com/questions/399868/why-does-larger-perplexity-tend-to-produce-clearer-clusters-in-t-sne ▬▬ Support me if you like 🌟 ►Link to this channel: https://bit.ly/3zEqL1W ►Support me on Patreon: https://bit.ly/2Wed242 ►Buy me a coffee on Ko-Fi: https://bit.ly/3kJYEdl ►E-Mail: [email protected] ▬▬ Used Music ▬▬▬▬▬▬▬▬▬▬▬ Music from #Uppbeat (free for Creators!): https://uppbeat.io/t/sulyya/weather-compass License code: ZRGIWRHMLMZMAHQI ▬▬ Used Icons ▬▬▬▬▬▬▬▬▬▬ All Icons are from flaticon: https://www.flaticon.com/authors/freepik ▬▬ Timestamps ▬▬▬▬▬▬▬▬▬▬▬ 00:00 Intro 00:30 Manifold learning 02:40 Relevant Papers & Agenda 03:25 Stochastic Neighbor Embedding (SNE) 03:56 Pairwise distances 04:35 Distance to Probability 06:06 Conditional Probability Math 07:05 Adjustment of Variance 08:20 Perplexity 09:55 How to find the variance 11:15 KL-divergence 12:55 Shepard Diagram 13:15 Gradient and it's interpretation 14:15 N-body simulation 14:35 Full SNE Algorithm 15:15 t-distributed Stochastic Neighbor Embedding (t-SNE) 15:28 Crowding Problem and how to solve it 17:58 Gaussian vs. Student's t Distribution 19:21 Symmetric Probabilities 20:35 Early Exaggeration 22:50 SNE vs. t-SNE 23:08 Brilliant.org Sponsoring 24:14 Code 27:15 Distill.pub Blogpost 27:49 Barnes-Hut t-SNE 29:54 Comparison 31:06 Outro ▬▬ My equipment 💻 - Microphone: https://amzn.to/3DVqB8H - Microphone mount: https://amzn.to/3BWUcOJ - Monitors: https://amzn.to/3G2Jjgr - Monitor mount: https://amzn.to/3AWGIAY - Height-adjustable table: https://amzn.to/3aUysXC - Ergonomic chair: https://amzn.to/3phQg7r - PC case: https://amzn.to/3jdlI2Y - GPU: https://amzn.to/3AWyzwy - Keyboard: https://amzn.to/2XskWHP - Bluelight filter glasses: https://amzn.to/3pj0fK2

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