Gradient descent is an optimization algorithm used to minimize a function by iteratively moving towards the steepest descent, or negative gradient, of the function. It is widely used in machine learning to update model parameters and minimize the loss function, ensuring the model learns efficiently from data.

Gradient Descent

Introduction to Regression Concepts and Applications

Understanding Machine Learning Algorithms in AI Engineering

Error-Driven Learning in AI Engineering

Mastering Efficient Gradient Descent for AI Optimization

The Role of Gradient Descent in Optimizing AI Models

Initialization of Machine Learning Models in C#

Introduction to Gradients

Understanding Machine Learning Algorithms

Building Neural Networks for Image Recognition

Understanding Machine Learning Basics

Machine Learning

Weights File

Linear Regression

Creating a Custom YOLO Model from Scratch in C#

Classification Loss

Data Normalization

Saturated Activation Functions

Exploring the Foundations and Future of Neural Networks

Building AI Models with Machine Learning

Softmax Function

Understanding Machine Learning and Its Role in AI

Deep Dive into Neural Networks and Architectures

Introduction to Linear Classification

The Comprehensive Guide to Understanding Machine Learning

Federated Learning in C#

Deep Learning Architectures and Their Applications in AI Engineering

First-Order Optimization

Introduction to Supervised Learning

Basic Components of ML Architectures

Concepts

Relevant Degrees