Document Analysis: Topic Modeling

Related lectures (31)

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

Covers the basics of machine learning, including supervised and unsupervised learning, linear regression, and classification.

Explores polynomial curve fitting, kernel functions, and regularization techniques, emphasizing the importance of model complexity and overfitting.

Introduces the basics of machine learning, covering supervised and unsupervised learning, linear regression, and data understanding.

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

Explores cross-validation, overfitting, regularization, and regression techniques in machine learning.

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

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