Stochastic gradient descent

Stochastic-gradient-descent

Stochastic Gradient Descent (SGD) is a widely used optimization algorithm in machine learning and deep learning. Unlike traditional gradient descent, which computes the gradient of the loss function using the entire dataset, SGD updates the model parameters using only a single training example at each iteration. This approach significantly reduces computational costs, making it particularly effective for large datasets. The randomness introduced by selecting individual data points helps SGD escape local minima and can lead to faster convergence. Variants like mini-batch gradient descent further enhance its efficiency by processing small batches of data, balancing speed and accuracy.

Dive intro Deep Learning Book

In earlier chapters we kept using stochastic gradient descent in our training procedure, however, without explaining why it works. To shed some light on it, we just described the basic principles of g...

1.5. Stochastic Gradient Descent

Scikit-learn User Guide

Stochastic Gradient Descent (SGD) is a simple yet very efficient approach to fitting linear classifiers and regressors under convex loss functions such as (linear) Support Vector Machines and Logis......

Stochastic Gradient Descent

Codecademy

In Sklearn , Stochastic Gradient Descent (SGD) is a popular optimization algorithm that focuses on finding the best set of parameters for a model that minimizes a given loss function. Unlike tradition...

Stochastic Gradient Descent — Clearly Explained !!

Towards Data Science

Stochastic gradient descent is a very popular and common algorithm used in various Machine Learning algorithms, most importantly forms the basis of Neural Networks. In this article, I have tried my…

Early stopping of Stochastic Gradient Descent

Scikit-learn Examples

Early stopping of Stochastic Gradient Descent Stochastic Gradient Descent is an optimization technique which minimizes a loss function in a stochastic fashion, performing a gradient descent step sampl...

Stochastic Gradient Descent: Explanation and Complete Implementation from Scratch

Towards Data Science

Stochastic gradient descent is a widely used approach in machine learning and deep learning. This article explains stochastic gradient descent using a single perceptron, using the famous iris…

Stochastic Gradient Descent (SGD)

Analytics Vidhya

Gradient Descent, a first order optimization used to learn the weights of classifier. However, this implementation of gradient descent will be computationally slow to reach the global minima. If you…

Stochastic Gradient Descent & Momentum Explanation

Towards Data Science

Let’s talk about stochastic gradient descent(SGD), which is probably the second most famous gradient descent method we’ve heard most about. As we know, the traditional gradient descent method…

Understanding Stochastic Gradient Descent in a Different Perspective

Towards Data Science

The stochastic optimization [1] is a prevalent approach when training a neural network. And based on that, there are methods like SGD with Momentum, Adagrad, and RMSProp, which can give decent…

Stochastic Gradient Descent: Math and Python Code

Towards Data Science

Deep Dive on Stochastic Gradient Descent. Algorithm, assumptions, benefits, formula, and practical implementation Image by DALL-E-2 Introduction The image above is not just an appealing visual that d...

Stochastic Gradient Descent with momentum

Towards Data Science

This is part 2 of my series on optimization algorithms used for training neural networks and machine learning models. Part 1 was about Stochastic gradient descent. In this post I presume basic…

Gradient Descent

Dive intro Deep Learning Book

In this section we are going to introduce the basic concepts underlying gradient descent . Although it is rarely used directly in deep learning, an understanding of gradient descent is key to understa...