A neural network is built from layers of small, simple units called neurons. Each neuron takes some numbers in, multiplies them by adjustable weights, adds them up, and passes the result through a function that decides how strongly to "fire." On its own, one neuron can barely do anything interesting.
The power comes from stacking layers of these neurons. Early layers might learn to detect simple things, like edges in an image. Later layers combine those simple detections into more complex ideas, like shapes, then objects, then whole scenes. This layered structure is where the word "deep" in deep learning comes from — deep meaning many layers, not "profound."
Deep learning became practical once GPUs made it affordable to train networks with dozens or hundreds of layers on huge datasets. That scale is why modern systems can tell a golden retriever from a labrador, transcribe speech in noisy rooms, or write coherent paragraphs of text.