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Dimensionality reduction

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Related lectures (31)

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Neural Networks Recap: Activation Functions

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

Dimensionality Reduction: PCA & Autoencoders

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

Clustering: Theory and Practice

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

PCA: Interactive class

On PCA includes interactive exercises and emphasizes minimizing information loss.

Kernel PCA: Nonlinear Dimensionality Reduction

Explores Kernel Principal Component Analysis, a nonlinear method using kernels for linear problem solving and dimensionality reduction.

Principal Component Analysis: Dimensionality Reduction

Explores Principal Component Analysis for dimensionality reduction in machine learning, showcasing its feature extraction and data preprocessing capabilities.

Clustering Methods and Dimensionality Reduction

Covers clustering methods and dimensionality reduction techniques.

Data Representation: PCA

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

Dimensionality Reduction

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

Clustering & Density Estimation

Covers clustering, PCA, LDA, K-means, GMM, KDE, and Mean Shift algorithms for density estimation and clustering.