Sparse neural networks since implementation of the encoder interception of a size of 8* 8 picture small patches given a lot of sheets natural picture Chinese Communist 10000, now need to use sparse autoencoder way to train a hidden layer of the network to learn Characteristics.

Sparse Autoencoder\starter\checkNumericalGradient.m
..................\.......\computeNumericalGradient.m
..................\.......\display_network.m
..................\.......\IMAGES.mat
..................\.......\initializeParameters.m
..................\.......\minFunc\ArmijoBacktrack.m
..................\.......\.......\autoGrad.m
..................\.......\.......\autoHess.m
..................\.......\.......\autoHv.m
..................\.......\.......\autoTensor.m
..................\.......\.......\callOutput.m
..................\.......\.......\conjGrad.m
..................\.......\.......\dampedUpdate.m
..................\.......\.......\example_minFunc.m
..................\.......\.......\example_minFunc_LR.m
..................\.......\.......\isLegal.m
..................\.......\.......\lbfgs.m
..................\.......\.......\lbfgsC.c
..................\.......\.......\lbfgsC.mexa64
..................\.......\.......\lbfgsC.mexglx
..................\.......\.......\lbfgsC.mexmac
..................\.......\.......\lbfgsC.mexmaci
..................\.......\.......\lbfgsC.mexmaci64
..................\.......\.......\lbfgsC.mexw32
..................\.......\.......\lbfgsC.mexw64
..................\.......\.......\lbfgsUpdate.m
..................\.......\.......\.ogistic\LogisticDiagPrecond.m
..................\.......\.......\........\LogisticHv.m
..................\.......\.......\........\LogisticLoss.m
..................\.......\.......\........\mexutil.c
..................\.......\.......\........\mexutil.h
..................\.......\.......\........\mylogsumexp.m
..................\.......\.......\........\repmatC.c
..................\.......\.......\........\repmatC.dll
..................\.......\.......\........\repmatC.mexglx
..................\.......\.......\........\repmatC.mexmac
..................\.......\.......\mchol.m
..................\.......\.......\mcholC.c
..................\.......\.......\mcholC.mexmaci64
..................\.......\.......\mcholC.mexw32
..................\.......\.......\mcholC.mexw64
..................\.......\.......\mcholinc.m
..................\.......\.......\minFunc.m
..................\.......\.......\minFunc_processInputOptions.m
..................\.......\.......\polyinterp.m
..................\.......\.......\precondDiag.m
..................\.......\.......\precondTriu.m
..................\.......\.......\precondTriuDiag.m
..................\.......\.......\rosenbrock.m
..................\.......\.......\taylorModel.m
..................\.......\.......\WolfeLineSearch.m
..................\.......\sampleIMAGES.m
..................\.......\sparseAutoencoderCost.m
..................\.......\train.m
..................\.......\weights.jpg
..................\.......\minFunc\logistic
..................\.......\minFunc
..................\starter
Sparse Autoencoder

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