Artificial Neural Networks (or Neural Networks) are modeled on the way neural networks of the biological brain function. They consist of a series of interconnected nodes, or “neurons,” which are essentially small processing units that can analyze and interpret data. These networks are trained to recognize patterns and make decisions based on that data, allowing them to perform tasks such as recognizing faces, translating languages, or even playing games.
Neural networks function by using input data to adjust the connections between the neurons to recognize patterns and make accurate predictions. For example, if you want a machine to recognize and identify cats in an image, its neural network must be fed, or trained on, a large number of cat images. Once a neural network has been trained, it can be used to make predictions or decisions based on new input data. Neural networks are used in a wide range of applications, including image recognition, Natural Language Processing (NLP), and even self-driving cars.
