Neural networks
Neural networks are a type of artificial intelligence inspired by the way our brains work. They are based on a network of interconnected nodes that work together to process information and learn from data. In recent years, neural networks have made great strides in solving complex problems, such as speech and image recognition and autonomous vehicles.
How do neural networks work?
A neural network is made up of a network of nodes, also called neurons. These neurons are interconnected and can transmit information to each other. The information is transmitted through weights that determine the strength of the connection between the neurons. The neural network receives input in the form of data, such as an image. The input passes through the network and is processed by the neurons. Each layer in the network performs various calculations to transform and abstract the input. In the end, the network returns an output, such as a label that matches the image.
Neural networks learn through a process known as backward propagation. During this process, the weights between neurons are adjusted to improve the accuracy of the output. The network is trained on a dataset of examples for which the correct output is known. By repeatedly adjusting the weights and checking the accuracy, the network can get better and better at recognizing patterns and making predictions.
