Neural Networks: From Fundamentals to Modern AI · Your First End-to-End Training — From Data to Prediction

The training loop: forward → loss → gradient → update

Your First End-to-End Training — From Data to Prediction

Introduction

The heart of every neural-network training is four repeating steps: forward (compute predictions), loss (measure error), gradient (compute derivatives w.r.t. weights), update (move weights). This lesson dissects that cycle, shows what exactly happens at each step, why the order is rigid, and what breaks if you skip any element. After this lesson you know the loop that will recur in EVERY chapter of this course.