Tutorial Breaks Down How a Neural Network Learns the Celsius-Fahrenheit Formula

A developer tutorial series explains the core training loop of a neural network using a minimal single-neuron model that converts Celsius to Fahrenheit. The article focuses on the final two steps of each training iteration: computing gradients via backpropagation and updating parameters using a stochastic gradient descent optimizer. Gradients indicate the direction and magnitude each parameter must shift to reduce prediction error, while the optimizer applies small, scaled nudges using a chosen learning rate. The accompanying Python code trains a one-layer PyTorch model over 20,000 epochs, starting from random weights and converging toward the true rule of multiplying by 1.8 and adding 32. A separate inference script then loads the saved checkpoint to make predictions without retraining.