Inside AI Training: Loss Functions, Backpropagation & Optimizers

How does artificial intelligence actually learn? In this video, we break down the real engineering behind AI training — no hype, no magic. Just math. We explore how large language models like ChatGPT are trained using loss functions, backpropagation, gradient descent, and optimization algorithms. How does a neural network measure error? How does it know which parameter to adjust? What role does the learning rate play? And why do models sometimes get stuck in local minima? In this episode, you’ll learn: • What a Loss Function really does • How Cross-Entropy measures prediction error • How Backpropagation distributes responsibility • The Chain Rule behind neural network training • What Gradient Descent actually computes • Why Learning Rate determines stability • The difference between SGD and Adam Optimizer • How massive GPU clusters train billion-parameter models AI is not magic. It is optimized matrix multiplication at scale. In the next episode, we move from software to hardware — exploring the silicon that powers modern AI: The GPU Wars. This is Axiom Tech. We analyze the gears behind intelligence.