ShinyaG

The objective of this part of the project is to develop a linear regression model for the given dataset using Gradient Descent methods. The regression model should be built using numpy, pandas, and sklearn. Problem Description: The problem statement :Karl Pearson conducted a survey around 1903 on the height of heights of fathers and their sons. The number of data points in the survey are 1078 samples. It is interesting to see whether son’s height can be predicted given father’s height. Train the linear regression model using each of the following methods: 1) Stochastic Gradient Descent 2) Gradient Descent 3) Mini-batch Gradient Descent

Language:Python100

Gradient-descent-project

Gradient Descent, Stochastic Gradient Descent & Mini Batch Gradient Dscent

Language:Python100

MachineLearningGradientDescent

Implement Gradient Descent and Stochastic Gradient Descent

Language:Python100

Neural-Networks

Gradient Descent, Stochastic Gradient Descent, Mini Batch Gradient Descent

Language:Python100

Linear-regression-with-stochastic-gradient-descent

Implement linear regression with (i) stochastic gradient descent. and (ii) stochastic mini-batch gradient descent. Comparing the results

Language:Python300

Gradient-Descent-Algorithms

Regular Gradient Descent, HogWild!, Variance-Reduced Stochastic Gradient Descent (SVRG)

Language:PythonApache-2.0400

SGDForLinearModels

This package implements the Stochastic Gradient Descent algorithm in Python for Linear Regression, Ridge Regression, Logistic Regression and Logistic Regression with L2 Regularization.

Language:PythonBSD-3-Clause1300

SVRG-pytorch

Implementation of Stochastic Variance Reduced Gradient Descent

Language:Python900

Gradient-descent-optimizer-variations

This repository contains implementation of stochastic gradient descent, SGD with momentum, Adagrad, RMSprop, Adam, Adamax optimizer from scratch using Python language.

Language:PythonMIT2700

gradient_descent_compare

梯度下降、随机梯度下降、批量梯度下降方法之间性能优缺点的对比

Language:Python300

Machine-Learning-Sec.2

线性回归算法，close-form, batch 梯度下降，mini-batch 梯度下降，随机梯度下降，RMSE

Language:Python200

lasso

Convex optimizers for LASSO, including subgradient, project gradient, proximal gradient, smooth method, lagrangian method and stochastic gradient descent variants.

Language:Python3700