Deep learning does not use dimensionality reduction because deep learning itself is a useful dimensionality reduction technique. Deep learning learns a compressed, nonlinear representation of the data through the hidden layers. PCA is restricted to a linear mapping, while deep learning can have nonlinear mappings.

Deep learning does not use dimensionality reduction because deep learning itself is a useful dimensionality reduction technique. Deep learning learns a compressed, nonlinear representation of the data through the hidden layers. Since Deep Learning can learn nonlinear mappings, it is a more flexible dimensionality reduction technique than PCA which restricted to linear mappings.