Gradient descent explained (Maths behind AI)

What is Gradient Descent and how do AI models actually learn? In this video, we visually explain **Gradient Descent**, one of the most important optimization algorithms in Machine Learning and Deep Learning. Gradient Descent is the method used by AI models to minimize error (loss) by adjusting parameters such as weights and biases. It works by iteratively moving in the direction of the steepest descent to find the minimum of a function. Through simple visual animations, this video demonstrates how Gradient Descent works step by step and how it helps neural networks improve their predictions over time. In this video you will learn: • What Gradient Descent is • How loss functions guide learning • How gradients indicate the direction of change • How parameters are updated during training • Why Gradient Descent is essential for Machine Learning and Deep Learning Understanding Gradient Descent is key to understanding how modern AI systems learn from data and improve performance. This channel explains AI concepts using clear visual explanations to make complex ideas simple and intuitive. Subscribe for more videos on: Artificial Intelligence, Machine Learning, Deep Learning, Neural Networks, and the mathematics behind AI. #artificialintelligence #machinelearning #linearalgebra #eigenvectors #aiexplained #agenticai #generativeai