To try everything Brilliant has to offer—free—for a full 30 days, visit The first 200 of you will get 20% off Brilliant’s annual premium subscription. (Video sponsered by ) ▬▬ Papers / Resources ▬▬▬ Colab Notebook: Entropy: Attractive / Repulsive Forces Gradient: t-SNE Parameters distill: Other great resources: - By the t-SNE author: - A good view on probability: - CalTech tutorial: - Great visuals: - SNE vs T-SNE: - t-SNE in raw numpy: - t-SNE in raw javascript: @nstrayer/t-sne-explained-in-plain-javascript - Video by the t-SNE author: Image Sources: - Perplexity image: ▬▬ Support me if you like 🌟 ►Link to this channel: ►Support me on Patreon: ►Buy me a coffee on Ko-Fi: ►E-Mail: deepfindr@ ▬▬ Used Music ▬▬▬▬▬▬▬▬▬▬▬ Music from #Uppbeat (free for Creators!): License code: ZRGIWRHMLMZMAHQI ▬▬ Used Icons ▬▬▬▬▬▬▬▬▬▬ All Icons are from flaticon: ▬▬ 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 Sponsoring 24:14 Code 27:15 Blogpost 27:49 Barnes-Hut t-SNE 29:54 Comparison 31:06 Outro ▬▬ My equipment 💻 - Microphone: - Microphone mount: - Monitors: - Monitor mount: - Height-adjustable table: - Ergonomic chair: - PC case: - GPU: - Keyboard: - Bluelight filter glasses: