Neural networks, a fundamental concept in artificial intelligence (AI), have revolutionized the way computers learn and solve complex problems. Modeled after the human brain, neural networks consist of interconnected nodes, or “neurons,” that process and transmit information through layers of computation. By analyzing vast amounts of data, neural networks can recognize patterns, make predictions, and perform tasks that were once thought to be exclusive to human intelligence. Here’s a closer look at neural networks, their inner workings, and their applications in various fields.
How Neural Networks Work: Neural networks are composed of layers of interconnected nodes, each of which performs a simple mathematical operation on the input data and passes the result to the next layer. The first layer receives the raw input data, such as images or text, while the subsequent layers extract increasingly abstract features and patterns from the data. This process continues until the final layer produces the desired output, such as a classification label or a numerical prediction. Through a process known as “training,” neural networks adjust the strength of connections between neurons to minimize errors and improve performance on a given task.
