Neuron
A tiny decision-maker. It adds up its inputs and decides how loudly to fire.
y = activation(Σ wᵢxᵢ + b)
Glossary
Every term in plain kid-friendly language, with the real math version underneath.
Layer
A row of neurons that all look at the same signal together.
h⁽ˡ⁾ = activation(W⁽ˡ⁾h⁽ˡ⁻¹⁾ + b⁽ˡ⁾)
Weight
A volume knob that says how much one input matters.
wᵢ — the multiplier applied to input xᵢ before summing
Bias
A head start added to a neuron so it can fire even when its inputs are quiet.
sum = Σ wᵢxᵢ + b; b shifts the threshold
Activation
The rule that turns a neuron's total into its final 'boom'.
e.g. ReLU: max(0, x), sigmoid: 1/(1+e⁻ˣ), tanh: (eˣ−e⁻ˣ)/(eˣ+e⁻ˣ)
Gain
A stretch knob. Higher gain makes the activation react more steeply.
y = activation(gain · x) — gain scales the input before the curve
Learning rate
How big a step the network takes when it fixes a mistake.
w ← w − α · ∂Loss/∂w, where α is the learning rate
Loss (error)
A score for how wrong the network's answer was — smaller is better.
Loss = (guess − correct)², averaged over examples
Class
A label we want the network to pick, like 'cat' or 'dog'.
y ∈ {A, B} — the category the output is matched against
Decision boundary
The dividing line where the network switches from saying 'this one' to 'that one'.
the set of points where Σ wᵢxᵢ + b = 0