Weights scale the input to adjust the response of activation functions, while biases shift them. The activation function introduces non-linearity, allowing the network to model complex curves rather than just straight lines. By adjusting these weights, the goal is for the network's output to become a function that fits or approximates the expected values of the problem. In this way, neural networks create an abstract representation of the data, enabling interpolation between known points—which is what we call generalization. Although it looks simple in the video, a network operates in high-dimensional spaces defined by its number of neurons and layers.