Part 5/9:

To illustrate this, consider a neural network designed for digit recognition using grayscale images of 28 by 28 pixels, which amounts to 784 inputs. The network's architecture consists of distinct layers: an input layer representing each pixel, hidden layers that process the information, and an output layer that indicates the recognized digit.

From Simple Functions to Complex Models

The simple design principle of the neural network evolves as one considers the multitude of parameters involved. Each connection between neurons possesses a weight, influencing how inputs adjust the network's output. This adaptation process, known as machine learning, essentially revolves around optimizing these weights to minimize error in predictions.