Stochastic gradient descent

Stochastic-gradient-descent

Stochastic Gradient Descent (SGD) is a powerful optimization algorithm widely used 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 data point or a small batch at each iteration. This introduces randomness into the optimization process, allowing for faster convergence and reduced computational costs, especially with large datasets. While SGD can be noisy and may not always guarantee convergence to the global minimum, it is particularly effective for training complex models like neural networks, making it a cornerstone of modern machine learning practices.

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 — 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...

Why Stochastic Gradient Descent Works?

Towards Data Science

Optimizing a cost function is one of the most important concepts in Machine Learning. Gradient Descent is the most common optimization algorithm and the foundation of how we train an ML model. But it…...