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), 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.

Version: 2.0
Published: 2016-12-29
Author: Dendi Handian, Imam Fachmi Nasrulloh, Lala Septem Riza, and Rani Megasari
Maintainer: Dendi Handian <dendi at student.upi.edu>
License: GPL-2 | GPL-3 | file LICENSE [expanded from: GPL (≥ 2) | file LICENSE]
URL: https://github.com/drizzersilverberg/gradDescentR
NeedsCompilation: no
CRAN checks: gradDescent results

Downloads:

Reference manual: gradDescent.pdf
Package source: gradDescent_2.0.tar.gz
Windows binaries: r-devel: gradDescent_2.0.zip, r-release: gradDescent_2.0.zip, r-oldrel: gradDescent_2.0.zip
OS X Mavericks binaries: r-release: gradDescent_2.0.tgz, r-oldrel: gradDescent_2.0.tgz

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