t-Distributed Stochastic Neighbor Embedding ( t-SNE) | Dimensionality Reduction | Explained скачать в хорошем качестве

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

🎥 Next Video: UMAP :-    • Uniform Manifold Approximation and Project...   👉 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:    • Maths for AI & ML   🕔 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