Dimensionality Reduction in Tamil | Conceptualisation | Machine learning #38
Dimensionality reduction techniques like PCA (Principal Component Analysis) and t-SNE (t-Distributed Stochastic Neighbor Embedding) are widely used in machine learning and data visualization to reduce the complexity of high-dimensional data while preserving certain aspects of the original data's structure.
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00:00:00 - Start
00:01:10 - 1. Why dimensionality Reduction
00:03:55 - 2. Data Representation
00:09:53 - 3.Covariance
00:18:54 - 4.MNIST Dataset
00:25:43 - 6.Geometrical Interpretation of PCA
00:31:30 - 7.Mathematical Objective Function of PCA
00:40:59 - 8.Eigen Values and Eigen Vectors
00:46:34 - 9.Geometrical Intepratation of λ
00:50:00 - 10.PCA for dimensioanality Reduction and Visualisation
00:54:35 - 11.Limitation of PCA
00:57:02 - 12.T-SNE Algorithm
01:00:34 - 13. Geometric Inution of T-SNE
01:02:41 - 14. Crowding Problem in T-SNE
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