Much like RBMs, autoencoders are a class of unsupervised learning algorithms that aim to uncover the hidden structures within data. In principal component analysis (PCA), we try to capture the linear relationships among input variables, and try to represent the data in a reduced dimension space by taking linear combinations (of the input variables) that account for most of the variance in data. However, PCA would not be able to capture the nonlinear relationships between the input variables.

Autoencoders are neural networks that can capture the nonlinear interactions between input variables while representing the input in different dimensions in a hidden layer. Most of the time, the dimensions of the hidden layer are smaller ...

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