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)
