From API to GPU, Week 6 (Part 2): Watching a Neural Network Learn
Phase 2 of 8: Enough ML to understand inference. Week 6 of 32, part 2 of 2. Part 1 built a one-neuron model that converts Celsius to Fahrenheit, made it predict, and scored how wrong it was with a single number, the loss. With random starting values the loss was 10352.21, because every prediction was nonsense. This post takes that one number and turns it into learning: the weight and bias climb…
Phase 2 of the eight-part series, Week 6 Part 2, explores the neural network learning process. After the first part demonstrated a one-neuron model converting Celsius to Fahrenheit, this post focuses on the learning aspects: weight and bias adjustments, loss minimization, gradient computation, and optimizer steps. The training loop consists of four stages repeated multiple times: forward pass, loss calculation, gradient computation, and optimizer update.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
