Understanding neural networks

Neural networks are computational models inspired by the brain. They consist of interconnected artificial neurons that learn complex patterns from data. By adjusting connection weights through backpropagation, neural networks minimize prediction errors during training. The hierarchical structure, including hidden layers, allows the networks to extract and model intricate relationships within the data. With their ability to process vast amounts of data and make accurate predictions, neural networks have proven valuable in domains like image recognition, natural language processing, and autonomous vehicles, pushing the boundaries of artificial intelligence.