Neural Network

A neural network is a type of algorithm used in artificial intelligence (AI) and machine learning, modeled after the structure of neurons (nerve cells) in the human brain. It consists of multiple "nodes (artificial neurons)" connected in layers, passing and transforming information from input to output. The network applies weights to input data and performs nonlinear transformations through activation functions to arrive at an output. During training, the weights of each node are adjusted using techniques such as backpropagation to improve the model's accuracy. The basic structure consists of three types of layers: • Input layer: Receives the data • Hidden layer(s): Extracts and transforms features between input and output (often multiple) • Output layer: Produces the final prediction or classification result Neural networks are the foundation of numerous AI technologies—including image classification, speech recognition, NLP, and chatbots—and in particular, a "deep neural network (DNN)" with many layers is the core of deep learning. Most of today's advanced AI systems have evolved on the foundation of neural networks, making them one of the most essential technologies in AI development.