Training a Single Neuron with Weight and Bias Updates

Summary

The article explains how to extend a linear model into a simple artificial neuron by adding an intercept, called bias, alongside the slope, called weight. It uses mean squared error over training examples as a cost function and estimates each parameter’s error by perturbing that parameter slightly, then adjusts both iteratively. The worked example applies this process to a two-input OR gate using a sigmoid output, illustrating that a small neuron can learn a nonlinear classification rule through parameter tuning.

The evidence is an implementation walkthrough and reported terminal output, rather than a systematic benchmark. The example uses a small, fixed dataset and a manually chosen perturbation size; the text gives little analysis of convergence behavior or robustness. It frames the approach as an accessible introduction to neural networks and notes that training can take time, while GPU acceleration is deferred until larger computations make it useful.

Key ideas

• The article treats a neuron's weight as the slope and its bias as the line's intercept.
• Mean squared error summarizes the mismatch between model output and training targets.
• Small parameter perturbations estimate how changing weight or bias affects the cost.
• The example trains a two-input neuron to reproduce an OR gate.
• The demonstration is introductory and does not establish performance on larger datasets.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.