Deep Generative Models: Variational Autoencoders & GANs

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Deep Generative Models

Covers deep generative models, including LDA, autoencoders, GANs, and DCGANs.

Machine Learning Fundamentals

Introduces fundamental machine learning concepts, covering regression, classification, dimensionality reduction, and deep generative models.

Document Analysis: Topic Modeling

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

Document Analysis and Topic Modeling

Covers document analysis, topic modeling, and deep generative models, including autoencoders and GANs.

Deep Generative Models

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

Deep Learning Modus Operandi

Explores the benefits of deeper networks in deep learning and the importance of over-parameterization and generalization.

Generative Models: Self-Attention and Transformers

Covers generative models with a focus on self-attention and transformers, discussing sampling methods and empirical means.

Machine Learning Review

Covers a review of machine learning concepts, including supervised learning, classification vs regression, linear models, kernel functions, support vector machines, dimensionality reduction, deep generative models, and cross-validation.

Deep Generative Models: Part 2

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

Gaussian Mixture Models: Data Classification

Explores denoising signals with Gaussian mixture models and EM algorithm, EMG signal analysis, and image segmentation using Markovian models.