Neural networks are algorithms designed to mimic how the human brain processes information — they’re the building blocks of modern AI systems.
The idea comes from neuroscience: just like our brains have neurons that send signals to each other, neural networks have nodes connected in layers. Each node receives input, does a little math, and passes the result to the next layer. By adjusting how much importance each input has (called weights), the network “learns” patterns — like recognizing faces, translating languages, or recommending videos.
There are different types: feedforward networks (simple and linear), convolutional networks (great for images), and recurrent networks (useful for sequences like text or sound). Training a neural network involves feeding it lots of data and adjusting the weights to reduce error, often using a process called backpropagation.
They power everything from voice assistants to image generators — basically, if it’s smart and digital, a neural network is probably involved.
More on neural networks from IBM
« Back to Glossary Index