gradDescent (2.0)

Gradient Descent for Regression Tasks.

https://github.com/drizzersilverberg/gradDescentR
http://cran.r-project.org/web/packages/gradDescent

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.

Maintainer: Dendi Handian
Author(s): Dendi Handian, Imam Fachmi Nasrulloh, Lala Septem Riza, and Rani Megasari

License: GPL (>= 2) | file LICENSE

Uses: Does not use any package

Released over 3 years ago.