Neuron Basics

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Single Neuron: Interactive Demo

A neuron receives inputs, multiplies each by a weight, adds a bias, and applies an activation function. Adjust the controls to see how the output changes and learn what each part means.

Concepts

  • Input: values from data or previous layer
  • Weight: strength of each input
  • Bias: shifts the sum
  • Activation: adds non-linearity
  • Output: result after activation

Live Calculation

Tips

Try changing inputs, weights, bias, and activation. See how the output changes and how activation affects the result.

Single Input Neuron (Visual)

x₁ w₁ + b activation output
Flow: Input (x₁) → Weight (w₁) → Add Bias (b) → Activation → Output

Neuron Concepts (Simple English)

  • Input: The number you give to the neuron (like x₁).
  • Weight: Shows how important the input is.
  • Bias: A number added to shift the result.
  • Activation: Changes the output, often making it non-linear.
  • Neuron: Takes input, multiplies by weight, adds bias, applies activation, and gives output.
  • Output: The final answer from the neuron.
This is how a single neuron works in a neural network. You can imagine it as a small calculator that transforms numbers step by step.