Neural Networks Explained: How AI Models Learn from Data

Neural networks are machine learning models made up of interconnected mathematical units called neurons, loosely inspired by how biological neurons in the human brain process and pass signals. A typical network consists of an input layer, one or more hidden layers, and an output layer that produces a final prediction such as a probability score. Each neuron calculates a weighted sum of its inputs, adds a bias value, and applies an activation function to determine its output. The weights and biases are not set manually but are learned automatically during a training process. Networks with many hidden layers are known as deep neural networks, which is the origin of the term deep learning.