sparse autoencoder written by matlab, through visualization step, you can find the features extracted by sparse coding .

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

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