Chapter 3. Autoencoders
In the previous chapter, Chapter 2, Deep Neural Networks, you were introduced to the concepts of deep neural networks. We're now going to move on to look at autoencoders, which are a neural network architecture that attempts to find a compressed representation of the given input data.
Similar to the previous chapters, the input data may be in multiple forms including, speech, text, image, or video. An autoencoder will attempt to find a representation or code in order to perform useful transformations on the input data. As an example, in denoising autoencoders, a neural network will attempt to find a code that can be used to transform noisy data into clean ones. Noisy data could be in the form of an audio recording with static noise which is then converted into clear sound. Autoencoders will learn the code automatically from the data alone without human labeling. As such, autoencoders can be classified under unsupervised learning algorithms...
