t-Distributed Stochastic Neighbor Embedding ( t-SNE) | Dimensionality Reduction | Explained

📘 Notes:- https://robosathi.com/docs/machine_learning/unsupervised/dimensionality_reduction/t-sne/
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🎥 Related Video: KL Divergence :- https://youtu.be/9n681xIlQU8

👉 In this video, we break down how t-SNE actually works,starting from its real-world use cases to the math intuition behind Gaussian and t-distributions.

🎯 Learning Objectives
✅ Understand what is t-SNE?
✅ Learn the role of Gaussian distribution in high-D space
✅ Understand why t-distribution is used in low-D space
✅ Compare Gaussian vs t-distribution intuitively
✅ See how gradient descent optimizes embeddings
👉 Maths for ML Playlist:
https://www.youtube.com/playlist?list=PLnpa6KP2ZQxePOg6k6vAkcg5Y50EAZds9
🕔 Time Stamp 🕘
00:00:00 - 00:00:25 Introduction
00:00:26 - 00:01:23 Use case of t-SNE
00:01:24 - 00:02:32 Intuition of t-SNE
00:02:33 - 00:03:35 What is t-SNE
00:03:36 - 00:09:56 Problem Solving
00:09:57 - 00:13:42 High Dimensional Space (Gaussian)
00:13:43 - 00:15:16 Low Dimensional Space ( t-Distribution)
00:15:17 - 00:17:47 Gaussian vs t-Distribution
00:17:48 - 00:18:36 Optimization
00:18:37 - 00:19:34 Gradient Descent
00:19:35 - 00:20:43 Meaning the Terms
00:20:44 - 00:23:44 Interpretation
00:23:45 - 00:26:49 Update Step
00:26:50 - 00:28:54 Perplexity
00:28:55 - 00:30:47 Drawbacks of t-SNE
00:30:48 - 00:31:27 What's Next? 🤔
#ai #ml #tsne #gradientdescent #perplexity

Видео t-Distributed Stochastic Neighbor Embedding ( t-SNE) | Dimensionality Reduction | Explained канала RoboSathi

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7 февраля 2026 г. 22:05:33

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