Neural network

A neural network is a computational model inspired by the functioning of the human brain's biological neural networks, designed to recognize patterns, learn from data, and make decisions. It consists of interconnected nodes or "neurons" organized into layers: an input layer that receives data, one or more hidden layers where processing occurs, and an output layer that produces the result.

Each connection between neurons has associated weights and biases that adjust during the learning process, and neurons apply activation functions to determine their outputs. Neural networks learn through a process called forward propagation, where data moves from input to output, and backpropagation, where errors are used to update the weights and biases to improve accuracy.

Various types of neural networks exist, including feedforward neural networks for general tasks, convolutional neural networks for image processing, and recurrent neural networks for sequential data. They are widely used in applications such as image and speech recognition, natural language processing, healthcare diagnostics, and financial analysis. Despite their adaptability and ability to learn complex patterns, neural networks require significant computational power and large datasets to perform well, and their decision-making processes can be opaque, presenting challenges in interpretability.