Autoencoder
An autoencoder is a type of neural network trained to reconstruct its own input by first compressing it into a smaller latent representation (encoding) and then reconstructing the original data from that representation (decoding).
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Glossary Terms(6)
Dimensionality Reduction
Dimensionality reduction is the process of transforming data with many input variables into a lower-dimensional representation that preserves as much meaningfu…
Principal Component Analysis
Principal Component Analysis (PCA) is a linear dimensionality reduction technique that transforms correlated variables into a smaller set of uncorrelated compo…
Autoencoder
An autoencoder is a type of neural network trained to reconstruct its own input by first compressing it into a smaller latent representation (encoding) and the…
Variational Autoencoder
A Variational Autoencoder (VAE) is a generative neural network that learns a probabilistic latent representation of data, enabling it to both reconstruct input…
Generative Adversarial Network
A Generative Adversarial Network (GAN) is a generative modeling framework in which two neural networks — a generator and a discriminator — are trained in compe…
Latent Space
Latent space is the compressed, lower-dimensional vector space a neural network learns internally to represent the underlying (latent) factors of variation in…