Each hidden or output node forms a weighted sum of the previous layer’s activations, adds its own bias term, and pushes the total through a non-linear activation function. Different functions give different activations. What per-neuron computation should the tester recount?

Short Explanation

Each hidden or output node forms a weighted sum of the previous layer, adds its own bias, and pushes the total through a non-linear activation function. That neuron bias is a learned term, not the Chapter 5 fairness kind. Hidden nodes do use a weighted sum.

Full Explanation

Per-neuron computation is a weighted sum, plus bias, through an activation function. That neuron bias is not the fairness kind. Omitting weights or turning the item into an implementation task is incorrect.

×