Neural networks are a machine learning model loosely inspired by the structure and function of the human brain. Neural networks are designed to recognize patterns and relationships in data and make predictions based on this information. A neural network comprises collections of interconnected nodes (organized into layers) called artificial neurons, which process and transmit information. Each artificial neuron receives inputs, performs a computation, and produces an output passed to the subsequent layers of neurons. The connections between the neurons are associated with weights and biases, which are adjusted during the training process to improve the accuracy of the predictions.
Neural networks are widely used in various applications of AI, such as image recognition, speech recognition, and natural language processing. They are helpful in solving complex, non-linear problems and can be trained on large and diverse datasets, allowing them to learn from a wide range of data.