What is a Neural Network?

A neural network is a type of machine learning model inspired by the structure of the human brain. It consists of layers of connected neurons, including an input layer, one or more hidden layers, and an output layer. Each layer transforms the input data (features) using an activation function to produce an output. In a standard neural network, the hidden layers apply these functions to learn patterns in the data, which are passed forward through the connections to the output layer. The output layer provides the final prediction or classification. This structure is often referred to as a Multilayer Perceptron (MLP), where each neuron in one layer is connected to every neuron in the next, allowing the network to learn complex relationships in data.

In simple terms, the neural network in the image is like a decision-making system. It starts with an input – in this case, a picture of a cat. The network looks at different features of the image (like the shape of the ears, fur, and body). These features pass through multiple layers (hidden layers) that analyze them in different ways.

By the time the information reaches the final layer (output layer), the network tries to decide if the picture is a cat, dog, or horse. Ideally, after processing the image, the network would "light up" the option for "cat" because it recognizes the patterns that match a cat's features.