Principal Component Analysis: Dimensionality Reduction

Related lectures (30)

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Introduces artificial neural networks, CNNs, and dimensionality reduction using PCA and autoencoders.

On PCA includes interactive exercises and emphasizes minimizing information loss.

Explores textual data classification, focusing on methods like Naive Bayes and dimensionality reduction techniques like Principal Component Analysis.

Introduces artificial neural networks and explores various dimensionality reduction techniques like PCA, LDA, Kernel PCA, and t-SNE.

Covers the basics of neural networks, activation functions, training, image processing, CNNs, regularization, and dimensionality reduction methods.

Explores PCA, Autoencoders, and their applications in dimensionality reduction and data generation.

Covers the theory and practice of clustering algorithms, including PCA, K-means, Fisher LDA, spectral clustering, and dimensionality reduction.

Covers the key concepts of PCA, including reducing data dimensionality and extracting features, with practical exercises.

Covers data representation using PCA for dimensionality reduction, focusing on signal preservation and noise removal.

Explains how PCA eliminates dimensions by finding principal components with most variation and compares PCA with Kernel PCA.