autoencoder
An autoencoder is a type of artificial neural network used to learn efficient representations of data, typically for the purpose of dimensionality reduction or feature learning. It operates under unsupervised learning, meaning it does not require labeled data for training. The network is composed of two main components: an encoder, which compresses the input data into a lower-dimensional representation, and a decoder, which reconstructs the data from this compressed form. The goal of an autoencoder is to minimize the difference between the original input and its reconstruction, often using a loss function like mean squared error. This process allows the autoencoder to capture the most important features of the input data[1][2][3].
Citations:
[1] https://en.wikipedia.org/wiki/Autoencoder
[2] https://deepai.org/machine-learning-glossary-and-terms/autoencoder
[3] https://towardsdatascience.com/auto-encoder-what-is-it-and-what-is-it-used-for-part-1-3e5c6f017726
[4] https://www.simplilearn.com/tutorials/deep-learning-tutorial/what-are-autoencoders-in-deep-learning
[5] https://www.tensorflow.org/tutorials/generative/autoencoder
[6] https://www.ibm.com/topics/autoencoder