What is a neural network?

A neural network is a computational model inspired by the structure, processing method, and learning ability of the human brain. Essentially, it is a framework for machine learning algorithms to process complex data inputs, learn from those inputs, and make decisions or predictions. Neural networks consist of layers of interconnected nodes, or neurons, which include an input layer, one or more hidden layers, and an output layer. Each connection between nodes has an associated weight, which is adjusted during the learning process.

When a neural network is being trained, it adjusts the weights based on the errors of its predictions, improving its performance over time. This process is known as “learning,” and it involves feeding the network with examples that have known outcomes. The network makes predictions based on its current state, compares its predictions to the known outcomes, and updates its weights to reduce the difference in future predictions.

Neural networks are capable of learning complex patterns and relationships within data, making them useful for a wide range of applications including image and speech recognition, natural language processing, medical diagnosis, stock market prediction, and many forms of classification and prediction tasks.