Are dimensionality reduction techniques useful in deep learning I have been working on machine learning and noticed that most of the time, dimensionality reduction techniques like PCA and t-SNE are used in machine learning, but I rarely noticed anyone doing it for deep learning projects. Is there a specific reason for not using dimensionality reduction techniques in deep learning?
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.