Neural Networks

Neural networks are computational models inspired by the human brain, designed to recognize patterns and learn from data. They consist of interconnected layers of nodes, or "neurons," which process input data and generate output. Each neuron receives input, applies a mathematical transformation, and passes the result to the next layer. Neural networks are particularly effective for tasks such as classification, regression, and function approximation.The basic structure includes an input layer, one or more hidden layers, and an output layer. During training, neural networks adjust the connections (weights) between neurons based on the error of the output compared to the expected result, typically using algorithms such as backpropagation. This process enables the network to learn from examples and improve its performance on specific tasks.Neural networks can vary in architecture, including feedforward networks, convolutional networks (often used in image processing), and recurrent networks (suitable for sequential data). Their ability to model complex relationships makes them a cornerstone of machine learning and artificial intelligence, with applications ranging from computer vision and natural language processing to autonomous systems.