Soft-taught leaning unsupervised learning is to learn the parameters of feature extraction, followed by supervised learning to train the classifier.

ExerciseSelf-Taught Learning\checkNumericalGradient.m
............................\computeNumericalGradient.m
............................\display_network.m
............................\feedForwardAutoencoder.m
............................\initializeParameters.m
............................\loadMNISTImages.m
............................\loadMNISTLabels.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
............................\softmaxCost.m
............................\softmaxExercise.m
............................\softmaxPredict.m
............................\sparseAutoencoderCost.m
............................\stlExercise.m
............................\t10k-images.idx3-ubyte
............................\t10k-labels.idx1-ubyte
............................\train-images.idx3-ubyte
............................\train-labels.idx1-ubyte
............................\minFunc\logistic
............................\minFunc
ExerciseSelf-Taught Learning

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