Weights and biases
The two kinds of knobs a network learns: weights scale each input, biases nudge the result.
Think of it like
A recipe where weights are how much of each ingredient and the bias is the pinch of salt added no matter what.
Example
Training a model means slowly turning billions of these weights and biases until the outputs stop being wrong.
How it actually works
Each neuron multiplies its inputs by weights, sums them, and adds a bias, then applies an activation. Weights decide what matters; the bias shifts the threshold for firing. Every “parameter” you hear about is one of these numbers, and learning is just adjusting them.
For product teams
“Number of parameters” is essentially a count of these knobs — a rough proxy for a model’s raw capacity.
For engineers
The learnable affine parameters W and b in y = Wx + b, updated by gradient descent.
Related
- Parameters — Collectively the parameters.
- Weights — The weight half is the weights.
- Bias term — The nudge is a bias term.
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