Gradient Descent for Regression Tasks

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Documentation for package ‘gradDescent’ version 3.0

• DESCRIPTION file.
• Code demos. Use demo() to run them.

Help Pages

ADADELTA	ADADELTA Method Learning Function
ADAGRAD	ADAGRAD Method Learning Function
ADAM	ADADELTA Method Learning Function
AGD	Accelerated Gradient Descent (AGD) Method Learning Function
GD	Gradient Descent (GD) Method Learning Function
gradDescentR.learn	GradDescent Learning Function
gradDescentRData	Data set of the package
MBGD	Mini-Batch Gradient Descent (MBGD) Method Learning Function
MGD	Momentum Gradient Descent (MGD) Method Learning Function
minmaxDescaling	Min-Max Scaling Revert Function
minmaxScaling	The Min-Max Feature Scaling Function
predict	The gradDescentR prediction stage
predict.gradDescentRObject	The gradDescentR prediction stage
prediction	Predicting Function for Linear Model
RMSE	RMSE Calculator Function
RMSPROP	ADADELTA Method Learning Function
SAGD	Stochastic Average Gradient Descent (SAGD) Method Learning Function
SARAH	Stochastic Recursive Gradient Algorithm (SARAH) Method Learning Function
SARAHPlus	Stochastic Recursive Gradient Algorithm+ (SARAH+) Method Learning Function
SGD	Stochastic Gradient Descent (SGD) Method Learning Function
splitData	The Data Spliting Function
SSGD	Semi Stochastic Gradient Descent (SSGD) Method Learning Function
SVRG	Stochastic Variance Reduce Gradient (SVRG) Method Learning Function
varianceDescaling	Variance/Standardization Revert Function
varianceScaling	The Variance/Standardization Feature Scaling Function