An oyster-farm intern draws a single unit that multiplies incoming measurements by learned numbers, adds a bias, and then squashes the result before passing it on. What are the learned numbers and the squash step for?
Select an answer to reveal the explanation.
Short Explanation
The intern drew a single unit. Weights and bias scale the inputs; the activation is a nonlinearity so the unit can bend off a straight line. Accuracy, GPU utilization, and a cooling setpoint are not those parts.
Full Explanation
In an artificial neuron, weights and bias scale the incoming values, and an activation function adds a nonlinearity. That is associate-level structure, not a calculus derivation. Accuracy, GPU utilization, splits, and datacenter setpoints are not those parts.