Deep Learning first battle: complete: ufldl tutorial sparse self-encoder-exercise: sparse autoencodercode: learned sparse parameter W1:
References:
- Ufldl tutorial sparse self-Encoder
Read autoencoders articles:
- [3] Hinton, G. E., osindero, S., & teh, Y. (2006). A fast learning algorithm for deep belief nets
- [4] Hinton,
G. E. and salakhudinov, R. R. cing the dimensionality of data with neural networks. Science 2006.
- If you want to play with the code, you can also find it at [5].
- [6] bengio,
Y., lamblin, P., popovici, P., larochelle, H. Greedy layer-wise training of deep networks. Nips 2006
- [7] Pascal
Vincent, Hugo larochelle, Yoshua bengio and Pierre-Antoine manzagol. Extracting and composing robust features with denoising autoencoders. icml 2008.
- (They have a nice model, but then backwards rationalize it into a probabilistic model. Ignore the backwards rationalized probabilistic model [Section 4].)
[4] rolling the dimensionality of data with neural networks, Hinton uses RBM to pre-training parameters [5] [6] greedy layer-wise training of deep networks, bengio demonstrates that RBM can be replaced by autoencoder to achieve considerable performance. It explores DBN training, continuous numerical input applicability, and dealing
With uncooperative input distributions. [7] extracting and composing robust features with denoising autoencoders
Processing images with noise/occlusion
