Package: gradDescent
Maintainer: Dendi Handian <dendi@student.upi.edu>
Type: Package
Title: Gradient Descent for Regression Tasks
Version: 2.0
URL: https://github.com/drizzersilverberg/gradDescentR
Date: 2016-12-22
Author: Dendi Handian, Imam Fachmi Nasrulloh, Lala Septem Riza, and Rani Megasari
Description: An implementation of various learning algorithms based on Gradient Descent for dealing with regression tasks.
The variants of gradient descent algorithm are :
Mini-Batch Gradient Descent (MBGD), an optimization to use training data partially to reduce the computation load.
Stochastic Gradient Descent (SGD), an optimization to use a random data in learning to reduce the computation load drastically.
Stochastic Average Gradient (SAG), a SGD-based algorithm to minimize stochastic step to average.
Momentum Gradient Descent (MGD), an optimization to speed-up gradient descent learning.
Accelerated Gradient Descent (AGD), an optimization to accelerate gradient descent learning.
Adagrad, a gradient-descent-based algorithm that accumulate previous cost to do adaptive learning.
Adadelta, a gradient-descent-based algorithm that use hessian approximation to do adaptive learning.
RMSprop, a gradient-descent-based algorithm that combine Adagrad and Adadelta adaptive learning ability.
Adam, a gradient-descent-based algorithm that mean and variance moment to do adaptive learning.
License: GPL (>= 2) | file LICENSE
RoxygenNote: 5.0.1
NeedsCompilation: no
Packaged: 2016-12-29 11:11:45 UTC; drizzer
Repository: CRAN
Date/Publication: 2016-12-29 14:10:00

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