Computational Chemistry
Autoencoders are a type of artificial neural network used for unsupervised learning, designed to learn efficient representations of data through a process of encoding and decoding. They compress input data into a lower-dimensional form, called the latent representation, before reconstructing it back to its original form. This ability to capture essential features of the data makes them particularly useful for tasks like noise reduction, anomaly detection, and dimensionality reduction in various applications.
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