Auto - Encoders
Autoencoders are: Artificial neural networks. Capable of learning efficient representations of the input data, called codings, without any supervision. The training set is unlabeled. These codings typically have a much lower dimensionality than the input data, making autoencoders useful for dimensionality reduction Why use Autoencoders? Useful for dimensionality reduction Autoencoders act as powerful feature detectors, And they can be used for unsupervised pre-training of deep neural networks Lastly, they are capable of randomly generating new data that looks very similar to the training data; this is called a generative model Surprisingly, autoencoders work by simply learning to copy their inputs to their outputs This may sound like a trivial task, but we will see that constraining the network in various ways can make it rather difficult For example You can limit the size of the internal representation, or you can add noise to the inputs and train th...