Master Gradient Descent: From Intuition to Advanced Variants

This comprehensive guide explains the mathematical foundation, intuitive intuition, algorithmic steps, tuning strategies, and variants of gradient descent, comparing it with other optimization methods and illustrating its use in machine‑learning models such as linear regression.

gradient descentlearning ratelinear regression

0 likes · 14 min read

Master Gradient Descent: From Intuition to Advanced Variants

Python Programming Learning Circle

Jan 11, 2022 · Artificial Intelligence

Dynamic Learning Rate Adjustment in PyTorch: Optimizer Basics and Scheduler Usage

This article explains how to configure and use PyTorch optimizers, their attributes and methods, and demonstrates various learning‑rate scheduling techniques—including manual updates and built‑in schedulers such as LambdaLR, StepLR, MultiStepLR, ExponentialLR, CosineAnnealingLR, and ReduceLROnPlateau—through clear code examples.

Deep LearningPyTorchScheduler

0 likes · 14 min read

Dynamic Learning Rate Adjustment in PyTorch: Optimizer Basics and Scheduler Usage

Python Programming Learning Circle

Dec 14, 2020 · Artificial Intelligence

Understanding Gradient Descent: Concept and Implementation

This article explains the intuition behind gradient descent, describing how the cost function surface is visualized, how the learning rate controls step size, and why updating parameters in the correct order is essential for converging to the optimal solution.

cost functiongradient descentlearning rate

0 likes · 4 min read

Understanding Gradient Descent: Concept and Implementation