Variational Autoencoders

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Related lectures (30)

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Generative Models: Self-Attention and Transformers

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

Deep Generative Models

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

Deep Generative Models: Part 2

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

Variational Auto-Encoders and NVIB

Explores Variational Auto-Encoders, Bayesian inference, attention-based latent spaces, and the effectiveness of Transformers in language processing.

Document Analysis: Topic Modeling

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

Dimensionality Reduction: PCA & Autoencoders

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

Natural Language Processing: Understanding Transformers and Tokenization

Provides an overview of Natural Language Processing, focusing on transformers, tokenization, and self-attention mechanisms for effective language analysis and synthesis.

Non-Linear Dimensionality Reduction

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

Deep Generative Models

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

Dimensionality Reduction: PCA and Kernel PCA

Covers PCA, Kernel PCA, and autoencoders for dimensionality reduction in data analysis.