Neural Network Theories

Neural networks are computer systems inspired by the human brain. They consist of interconnected nodes, or "neurons," that process information in layers. These networks learn from data by identifying patterns, enabling them to make predictions or classifications. For example, they can recognize images or understand speech. The learning process involves adjusting connections (weights) based on errors, akin to how we learn from experiences. Neural networks are a key component of artificial intelligence, powering applications from virtual assistants to self-driving cars. Their ability to improve with more data makes them powerful tools for solving complex problems.