Learn how to implement SGD with momentum from scratch in Python—boost your optimization skills for deep learning.

The first chapter of Neural Networks, Tricks of the Trade strongly advocates the stochastic back-propagation method to train neural networks. This is in fact an instance of a more general technique ...

The goal of a machine learning regression problem is to predict a single numeric value. For example, you might want to predict a person's bank savings account balance based on their age, years of ...

According to Yann LeCun (@ylecun), choosing a batch size of 1 in machine learning training can be optimal depending on the definition of 'optimal' (source: @ylecun, July 11, 2025). This approach, ...

Abstract: This letter presents a novel stochastic gradient descent algorithm for constrained optimization. The proposed algorithm randomly samples constraints and components of the finite sum ...

Understand what is Linear Regression Gradient Descent in Machine Learning and how it is used. Linear Regression Gradient Descent is an algorithm we use to minimize the cost function value, so as to ...

A new technical paper titled “Learning in Log-Domain: Subthreshold Analog AI Accelerator Based on Stochastic Gradient Descent” was published by researchers at Imperial College London. “The rapid ...

Adam is widely used in deep learning as an adaptive optimization algorithm, but it struggles with convergence unless the hyperparameter β2 is adjusted based on the specific problem. Attempts to fix ...