A neural network is a computing system inspired by the human brain. Unlike traditional programs that follow specific instructions, neural networks learn to recognize patterns in data and make decisions on their own. They are a key technology behind artificial intelligence applications we use every day.
Think of learning to cook spaghetti. You show someone 100 photos of spaghetti dishes - some perfect, some burnt, some undercooked. They guess if each is good or bad, get feedback when wrong, and adjust their cooking rules. After many trials, they learn the perfect recipe, even for new dishes they've never seen. This is exactly how a neural network learns - through examples, feedback, and gradual improvement.
A neural network has a layered structure. The input layer receives data, like pixels of an image. Hidden layers process this data step by step, detecting edges, then shapes, then objects. The output layer gives the final result. Neurons are like brain cells that receive inputs, do calculations, and pass outputs. Connections between neurons carry information and have adjustable weights that determine how strongly signals are passed along.
Neural networks learn through a process called training. First, they make a guess about the data. Then they check if the guess is correct and calculate how wrong they were. Based on this error, they adjust their internal weights and connections. This process repeats thousands of times until the network becomes accurate. As training progresses, errors decrease and accuracy increases, just like learning from mistakes.
Neural networks power many technologies we use daily. Your phone uses them for face recognition to unlock the screen. Google Translate uses neural networks to understand and translate languages. Self-driving cars rely on them to recognize traffic signs, pedestrians, and obstacles. In medicine, they help doctors diagnose diseases from X-rays and scans. Voice assistants like Siri understand speech through neural networks. In essence, neural networks are pattern-detecting machines that learn from examples, making our digital world smarter and more intuitive.