gradDescent: Gradient Descent for Regression Tasks

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), which is an optimization to use training data partially to reduce the computation load. Stochastic Gradient Descent (SGD), which is an optimization to use a random data in learning to reduce the computation load drastically. Stochastic Average Gradient (SAG), which is a SGD-based algorithm to minimize stochastic step to average. Momentum Gradient Descent (MGD), which is an optimization to speed-up gradient descent learning. Accelerated Gradient Descent (AGD), which is an optimization to accelerate gradient descent learning. Adagrad, which is a gradient-descent-based algorithm that accumulate previous cost to do adaptive learning. Adadelta, which is a gradient-descent-based algorithm that use hessian approximation to do adaptive learning. RMSprop, which is a gradient-descent-based algorithm that combine Adagrad and Adadelta adaptive learning ability. Adam, which is a gradient-descent-based algorithm that mean and variance moment to do adaptive learning.

Version: 2.0.1
Published: 2017-03-11
Author: Dendi Handian, Imam Fachmi Nasrulloh, Lala Septem Riza
Maintainer: Lala Septem Riza <lala.s.riza at upi.edu>
License: GPL-2 | GPL-3 | file LICENSE [expanded from: GPL (≥ 2) | file LICENSE]
URL: https://github.com/drizzersilverberg/gradDescentR
NeedsCompilation: no
In views: MachineLearning
CRAN checks: gradDescent results

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Reference manual: gradDescent.pdf
Package source: gradDescent_2.0.1.tar.gz
Windows binaries: r-devel: gradDescent_2.0.1.zip, r-release: gradDescent_2.0.1.zip, r-oldrel: gradDescent_2.0.1.zip
OS X El Capitan binaries: r-release: gradDescent_2.0.1.tgz
OS X Mavericks binaries: r-oldrel: gradDescent_2.0.1.tgz
Old sources: gradDescent archive

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