PS: These are notes from my study/review mostly from AI learning mode. Core Concepts Neuron = weights + bias + activation function. Weights: Measure how much each input matters. Bias: Shifts the threshold a neuron needs to “fire.” Activation function: Provides non-linearity, letting networks learn curves, not just straight lines. Single Neuron Example Compute the raw sum: $$ z = w_1x_1 + w_2x_2 + … + b $$. Apply activation (e.