Neural Network
A computing system inspired by the human brain that learns patterns from data through interconnected nodes organized in layers.
How Neural Networks Work
Think of a neural network like a decision-making machine built from tiny building blocks called neurons. These neurons are arranged in layers - an input layer that receives data, hidden layers that process it, and an output layer that produces results. Each connection between neurons has a weight that determines how much influence one neuron has on another.
When data flows through the network, each neuron takes inputs, multiplies them by weights, adds them up, and passes the result through an activation function. This function decides whether the neuron should "fire" and pass information to the next layer. It's loosely inspired by how biological neurons in your brain communicate through electrical signals.
Why Neural Networks Matter
Neural networks are the foundation of modern AI. They power everything from spam filters and recommendation systems to image recognition and language translation. What makes them special is their ability to learn from examples rather than following explicit rules. Show a neural network thousands of cat photos, and it'll figure out what makes a cat a cat.
The real magic happens during training. The network makes predictions, compares them to correct answers, and adjusts its weights to do better next time. This process repeats millions of times until the network becomes accurate. Once trained, it can make predictions on new data it's never seen before.