Nuclear Fusion Technology
Autoencoders are a type of artificial neural network used to learn efficient representations of data, typically for the purpose of dimensionality reduction or feature learning. They consist of two main parts: an encoder that compresses the input data into a smaller representation and a decoder that reconstructs the output from this compressed representation. This process can reveal hidden patterns in the data, making autoencoders particularly useful in machine learning applications, including those in fusion research.
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