Gradient Descent Explained Simply: How AI Models Actually Learn

▶ Deep Learning Foundations Playlist (start here):    • What is Artificial Intelligence, Machine L...   In this video, I break down Gradient Descent — the core idea behind how neural networks actually learn. You’ve probably heard terms like optimizer, loss, learning rate, SGD, Adam… but what do they really mean? Here’s the simple explanation: A model starts with random weights, measures how wrong it is (loss), and then uses gradient descent to update weights step-by-step toward better predictions. In this lesson, you’ll understand: What gradient descent is (intuition-first) Why the learning rate matters (too small vs too big) Why different optimizers exist (SGD, Momentum, RMSProp, Adam) This is part of my Deep Learning Foundations series, designed to make AI concepts feel clear, approachable and intuitive. If this helped you, consider subscribing — I’m building this channel to help you understand AI concepts clearly, not just use AI on a high level. Full Course (Deep Learning Mastery on Udemy): https://www.udemy.com/course/deep-lea...