ResNet is a type of artificial neural network that is particularly useful for tasks that require a lot of processing power, such as image recognition or language translation. It works by breaking down a task into smaller pieces and then processing each piece separately, which makes it more efficient and accurate than other types of neural networks.
The Res in ResNet stands for Residual, which refers to a unique feature of this model. In most AI models, the data is processed through a series of interconnected layers, called neurons, that are designed to recognize specific patterns. However, in a ResNet model, the neurons are connected in such a way that some of the data is residual, meaning it is not processed through all of the layers. This allows the model to recognize patterns that are more complex or subtle, making it more accurate and efficient at tasks such as image or video recognition.
