We show how nonlinear embedding algorithms popular for use with shallow semi-supervised learning techniques such as kernel methods can be applied to deep multi-layer architectures, either as a regularizer at the output layer, or on each layer of the architecture. This provides a simple alternative to existing approaches to deep learning such as autoassociators or density estimation, whilst yielding competitive error rates compared to those methods, and existing shallow semi-supervised techniques.