Dimensionality Reduction: PCA and Kernel PCA

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

Explores autoencoders, from linear mappings in PCA to nonlinear mappings, deep autoencoders, and their applications.

Explores document analysis, topic modeling, and generative models for data generation in machine learning.

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

Introduces unsupervised learning through clustering with K-means and dimensionality reduction using PCA, along with practical examples.

Covers deep generative models, including variational autoencoders, GANs, and deep convolutional GANs.

Covers non-linear dimensionality reduction techniques using autoencoders, deep autoencoders, and convolutional autoencoders for various applications.

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

Explores unsupervised learning techniques for reducing dimensions in data, emphasizing PCA, LDA, and Kernel PCA.

Explores deep generative models, including mixtures of multinomials, PCA, deep autoencoders, convolutional autoencoders, and GANs.