What are Autoencoders?
Autoencoders are a type of artificial neural network used primarily for unsupervised learning. They are designed to learn efficient representations of data by transforming input into a compressed form and then reconstructing the output from this representation. The fundamental structure of an autoencoder consists of two main components: the encoder and the decoder.
Encoder
The encoder compresses the input data into a lower-dimensional space or latent representation. This process captures the essential features of the input while reducing noise and redundancy.
Decoder
The decoder takes the compressed representation produced by the encoder and attempts to reconstruct the original input. The goal is to minimize the difference between the input and output data, usually measured by a loss function like Mean Squared Error.
Applications
Autoencoders can be applied in various fields, including image compression, denoising, and anomaly detection. They play a significant role in dimensionality reduction techniques and feature extraction, which are essential for improving the performance of machine learning models.
Overall, autoencoders are valuable tools in deep learning, facilitating efficient data processing and representation learning.