def submit(): conf = {} conf['assignmentSlug'] = 'k-means-clustering-and-pca' conf['itemName'] = 'K-Means Clustering and PCA' conf['partArrays'] = [ [ '1', ['findClosestCentroids.m'], 'Find Closest Centroids (k-Means)', ], [ '2', ['computeCentroids.m'], 'Compute Centroid Means (k-Means)', ], [ '3', ['pca.m'], 'PCA', ], [ '4', ['projectData.m'], 'Project Data (PCA)', ], [ '5', ['recoverData.m'], 'Recover Data (PCA)', ], ] conf['output'] = output submitWithConfiguration(conf)
def submit(): conf = {} conf['assignmentSlug'] = 'neural-network-learning' conf['itemName'] = 'Neural Networks Learning' conf['partArrays'] = [ [ '1', ['nnCostFunction.m'], 'Feedforward and Cost Function', ], [ '2', ['nnCostFunction.m'], 'Regularized Cost Function', ], [ '3', ['sigmoidGradient.m'], 'Sigmoid Gradient', ], [ '4', ['nnCostFunction.m'], 'Neural Network Gradient (Backpropagation)', ], [ '5', ['nnCostFunction.m'], 'Regularized Gradient', ], ] conf['output'] = output submitWithConfiguration(conf)
def submit(): conf = {} conf['assignmentSlug'] = 'support-vector-machines' conf['itemName'] = 'Support Vector Machines' conf['partArrays'] = [ [ '1', ['gaussianKernel.m'], 'Gaussian Kernel', ], [ '2', ['dataset3Params.m'], 'Parameters (C, sigma) for Dataset 3', ], [ '3', ['processEmail.m'], 'Email Preprocessing', ], [ '4', ['emailFeatures.m'], 'Email Feature Extraction', ], ] conf['output'] = output submitWithConfiguration(conf)
def submit(): conf = {} conf['assignmentSlug'] = 'multi-class-classification-and-neural-networks' conf['itemName'] = 'Multi-class Classification and Neural Networks' conf['partArrays'] = [ [ '1', ['lrCostFunction.m'], 'Regularized Logistic Regression', ], [ '2', ['oneVsAll.m'], 'One-vs-All Classifier Training', ], [ '3', ['predictOneVsAll.m'], 'One-vs-All Classifier Prediction', ], ['4', ['predict.m'], 'Neural Network Prediction Function'], ] conf['output'] = output submitWithConfiguration(conf)
def submit(): conf = {} conf['assignmentSlug'] = 'regularized-linear-regression-and-bias-variance' conf['itemName'] = 'Regularized Linear Regression and Bias/Variance' conf['partArrays'] = [ [ '1', ['linearRegCostFunction.m'], 'Regularized Linear Regression Cost Function', ], [ '2', ['linearRegCostFunction.m'], 'Regularized Linear Regression Gradient', ], [ '3', ['learningCurve.m'], 'Learning Curve', ], [ '4', ['polyFeatures.m'], 'Polynomial Feature Mapping', ], [ '5', ['validationCurve.m'], 'Validation Curve', ], ] conf['output'] = output submitWithConfiguration(conf)
def submit(): conf = {} conf['assignmentSlug'] = 'linear-regression' conf['itemName'] = 'Linear Regression with Multiple Variables' conf['partArrays'] = [ [ '1', ['warmUpExercise.m'], 'Warm-up Exercise', ], [ '2', ['computeCost.m'], 'Computing Cost (for One Variable)', ], [ '3', ['gradientDescent.m'], 'Gradient Descent (for One Variable)', ], [ '4', ['featureNormalize.m'], 'Feature Normalization', ], [ '5', ['computeCostMulti.m'], 'Computing Cost (for Multiple Variables)', ], [ '6', ['gradientDescentMulti.m'], 'Gradient Descent (for Multiple Variables)', ], [ '7', ['normalEqn.m'], 'Normal Equations', ], ] conf['output'] = output submitWithConfiguration(conf)
def submit(): conf = {} conf['assignmentSlug'] = 'anomaly-detection-and-recommender-systems' conf['itemName'] = 'Anomaly Detection and Recommender Systems' conf['partArrays'] = [ [ '1', ['estimateGaussian.m'], 'Estimate Gaussian Parameters', ], [ '2', ['selectThreshold.m'], 'Select Threshold', ], [ '3', ['cofiCostFunc.m'], 'Collaborative Filtering Cost', ], [ '4', ['cofiCostFunc.m'], 'Collaborative Filtering Gradient', ], [ '5', ['cofiCostFunc.m'], 'Regularized Cost', ], [ '6', ['cofiCostFunc.m'], 'Regularized Gradient', ], ] conf['output'] = output submitWithConfiguration(conf)
def submit(): conf = {} conf['assignmentSlug'] = 'logistic-regression' conf['itemName'] = 'Logistic Regression' conf['partArrays'] = [ [ '1', ['sigmoid.m'], 'Sigmoid Function', ], [ '2', ['costFunction.m'], 'Logistic Regression Cost', ], [ '3', ['costFunction.m'], 'Logistic Regression Gradient', ], [ '4', ['predict.m'], 'Predict', ], [ '5', ['costFunctionReg.m'], 'Regularized Logistic Regression Cost', ], [ '6', ['costFunctionReg.m'], 'Regularized Logistic Regression Gradient', ], ] conf['output'] = output submitWithConfiguration(conf)