Autoencoder.md (1203B)
1 # Autoencoder 2 3 ML General 4 5 **Definition:** An autoencoder is an unsupervised neural network that takes inputs, compresses them into a smaller representation while trying to maintain as much information as possible, and then reconstructs the compressed representation into a new full representation. 6 7 The idea of an autoencoder is for the model to learn the best way to extract features out of a large input (many features) so it can then be passed to another model that will require less features and subsequently be faster to train and use. 8 9 Autoencoder are made of two part they have an encoder and a decoder. The encoder takes in an input with all of the features and then outputs a compressed representation of it where the output has less features. The decoder then takes the compressed representation as the input and tries to create the original input to the encoder. The error (difference between output and actual input) is what we are trying to minimize. 10 11 Autoencoders are often used for unsupervised pretraing by training the autoencoder and then using the lower layers of it as the lower layers of a neural network. This uses the encoders compression as the input for the neural network.