Gradient Descent: The Algorithm That Trains Every AI Model

Gradient descent is the engine behind every AI model ever trained. From ChatGPT to Stable Diffusion to self-driving cars — they all learn using the same simple rule: follow the slope downhill. In this episode, we break down exactly how it works: partial derivatives, the update rule, the learning rate, and modern tricks like momentum, Adam, and stochastic gradient descent. 🎯 CHAPTERS 00:00 Why gradient descent is the engine of AI 0:45 The blindfolded hiker intuition 1:45 Partial derivatives and the gradient 3:15 The update rule: w ← w − α∇L 4:30 The learning rate — Goldilocks territory 6:00 Watching gradient descent learn a line 7:30 When the surface isn't a nice bowl 9:00 Momentum and Adam: modern tricks 11:00 Stochastic gradient descent (SGD) 12:30 From 2 parameters to GPT-4 📌 KEY CONCEPTS • Partial derivatives and the gradient vector • The update rule: new w = old w − α × ∂L/∂w • Learning rate: too small, too big, just right • Local minima, saddle points, plateaus • Momentum and Adam optimizer • Stochastic gradient descent (mini-batches) 📚 PREREQUISITE • Episode 1 — Linear Regression: youtu.be/... 📚 THE SERIES Episode 2 of 10. Next: Neural Networks — how stacking linear functions with one simple trick lets us learn anything. 📱 WANT TO PRACTICE THE MATH? NovaMaths — SAT & ACT Math prep with 749+ exercises: https://www.novamaths.app 🌍 French channel: ‪@MathsAcademy27‬ ───────────────────── Channel hosted by Julien, certified math teacher with 30 years of classroom experience. #GradientDescent #Adam #SGD #MachineLearning #AIMath