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Autoencoder - Wikipedia

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An autoencoder is a type of artificial neural network used to learn efficient codings of unlabeled data (unsupervised learning). An autoencoder learns two functions: an encoding function that transforms the input data, and a decoding function that recreates the input data from the encoded representation. The autoencoder learns an efficient representation (encoding) for a set of data, typically for dimensionality reduction, to generate lower-dimensional embeddings for subsequent use by other machine learning algorithms.[1] Variants exist which aim to make the learned representations assume useful properties.[2] Examples are regularized autoencoders (sparse, denoising and contractive autoencoders), which are effective in learning representations for subsequent classification tasks,[3] and variational autoencoders, which can be used as generative models.[4] Autoencoders are applied to many problems, including facial recognition,[5] feature detection,[6] anomaly detection, and learning the

Autoencoder - Wikipedia Jump to content From Wikipedia, the free encyclopedia Neural network that learns efficient data encoding in an unsupervised manner Not to be confused with Autocoder or Autocode . A schema of an autoencoder . An autoencoder has two main parts: an encoder that maps the message to a code, and a decoder that reconstructs the message from the code. Part of a series on Machine learning and data mining Paradigms Supervised learning Unsupervised learning Semi-supervised learning Self-supervised learning Reinforcement learning Meta-learning Online learning Batch learning Curricu

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