Neural networks are a type of machine learning algorithm that are inspired by the way the human brain works. They use a layered structure of interconnected nodes called neurons to process information in a way that allows them to learn from data.
Think of it like this: if you wanted to teach a computer to recognize images of cats, you would feed it thousands of images of cats and tell it which ones are cats and which ones aren’t. The computer would then use neural networks to analyze the images and learn what features are common among all the cat images. Once it has learned these features, it can use them to recognize new images of cats with a high degree of accuracy.
Neural networks consist of layers of interconnected nodes called neurons that process information in a way that allows them to learn from data. Each layer processes the data in a different way, allowing the network to learn increasingly complex features of the data.
For example, the first layer might process simple features like edges and lines, while later layers process more complex features like shapes and patterns. The output of the final layer is a prediction or classification based on the input data.
Neural networks use a process called backpropagation to adjust the weights of the connections between neurons in response to errors in their predictions. This allows them to learn from their mistakes and improve their accuracy over time.
Neural networks have been used in many different applications, including image recognition, speech recognition, natural language processing, and autonomous vehicles .