A neural network is a model built from layers of simple units that each apply a weight to their inputs, learning those weights from examples.
A neural network is the architecture. Deep learning is the practice of using many layers and training at scale.
A neural network is a model made of simple units arranged in layers. Each unit multiplies its inputs by learned weights, adds them up, and passes the result on.
No single unit does anything clever. The capability comes from stacking thousands of them, so that early layers pick up simple patterns and later layers combine those into complicated ones.
Think of it like this. Think of a huge committee where every member votes on one narrow question and passes their verdict up a level. Nobody sees the whole problem. The decision emerges from how the votes are weighted.