How do neural networks work?

Neural networks are a type of machine learning model inspired by the structure and functions of the human brain. These networks consist of interconnected nodes, or neurons, that receive inputs and produce outputs. Each neuron takes in inputs, processes them using mathematical operations, and sends its output to other neurons in the network. With each iteration, the network adjusts the weights of the connections between neurons to improve its accuracy in predicting an output. Essentially, neural networks learn from data to make predictions or classifications, and their ability to adapt and learn from experience makes them a powerful tool in fields such as image and speech recognition, natural language processing, and more.

This mind map was published on 19 June 2023 and has been viewed 60 times.