There exist a lot of different architectures of autoencoders distinguished by cost functions used to capture data representation. The most basic autoencoder is known as a vanilla autoencoder. It's a two-layer neural network with one hidden layer the same number of nodes at the input and output layers, with an objective to minimize the cost function. The typical choices, but not limited to, for a loss function are mean square error (MSE) for regression and cross entropy for classification. The current approach can be easily extended to multiple layers, also known as multilayer autoencoder.
The number of nodes plays a very critical role in autoencoders. If the number of nodes in the hidden layer is less than the input layer then an autoencoder is known as an under-complete autoencoder. A higher number of nodes in the hidden layer represents an over-complete...
