How deeply does Data Without Labels cover variational autoencoder?

Defines probabilistic latent-variable models and their training approach, but defers the mathematical details.

Variational autoencoder

A variational autoencoder is a neural-network generative model that learns a probabilistic latent representation and reconstructs data from sampled latent variables.

A variational autoencoder is a probabilistic generative variation of the standard autoencoder, which represents inputs using a compressed bottleneck. It models latent variables with probabilistic distributions, usually Gaussian, and uses neural networks as part of its structure. section 9.7

Training uses expectation-maximization meta-algorithms. The mathematical details are beyond the scope of this treatment. section 9.7

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Build a Text-to-Image Generator (from Scratch)A variational autoencoder is a generative neural model that encodes data into a lower-dimensional probabilistic latent representation and decodes that representation into reconstructed or generated data.
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Causal AIA variational autoencoder is a generative neural-network model that combines an encoder-defined approximating distribution with a decoder-defined generative distribution over observed data and latent variables.
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Learn Generative AI with PyTorchA generative model that learns an encoder-decoder latent-variable representation so it can reconstruct inputs and sample new outputs from latent space.
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Outlier Detection in PythonA variational autoencoder is a generative neural-network model that learns a probabilistic latent representation and reconstructs or generates observations from it.
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Math and Architectures of Deep LearningAn autoencoder that models the latent space probabilistically and is trained with regularizing constraints.
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Deep Learning with Python, Third EditionA generative latent-variable model that learns an encoder-decoder structure and a probabilistic latent space for reconstructing and sampling data.
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Evolutionary Deep Learning: Genetic algorithms and neural networksA variational autoencoder is a neural network that encodes inputs as probability distributions, samples from those distributions, and decodes the samples to reconstruct or generate data.
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Generative AI in ActionA variational autoencoder is a generative neural model that encodes data into a probabilistic latent representation and decodes samples from that representation into reconstructed or newly generated data.
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Deep Learning with R, Third EditionA variational autoencoder is a probabilistic autoencoder whose encoder represents inputs as parameters of a latent probability distribution and whose decoder reconstructs or generates data from sampled latent points.
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Machine Learning Algorithms in DepthA variational autoencoder is a generative neural network that learns an approximate latent-variable posterior and reconstructs observations from sampled latent representations.
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Machine Learning for Tabular Data: XGBoost, Deep Learning, and AIA variational autoencoder is a neural network architecture that encodes data into a probabilistic latent space and decodes latent representations to reconstruct or generate data.
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Graph Neural Networks in ActionA variational autoencoder is a probabilistic autoencoder that learns a parameterized latent-variable distribution and decodes samples from it to reconstruct or generate data.

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Related concepts

encoderhas part · An encoder is a component that transforms input data into an encoded representation for subsequent processing, reconstruction, comparison, or transmission.
autoencoderkind of · An autoencoder is a neural network that encodes input data into an internal representation and decodes it to reconstruct the input or an approximation.
decodehas part · Decoding is the process of transforming an encoded or internal representation into an interpretable or target-form output.
generationkind of · Generation is the computational production of outputs from supplied inputs or context, often by applying a model to produce predictions or new content.
reconstruction lossuses · Reconstruction loss is a measure of the discrepancy between an input or observation and the output a model reconstructs from it or its representation.
image generationuses · Image generation is the computational creation of new images from learned models, input data, or specified conditions.

This explainer was generated by Manning's knowledge graph from the text of Data Without Labels. The section links point to the parts of the book it draws on.