Briefly introduce Model AlexNet model

AlexNet is a deep convolutional neural network (CNN) that was introduced in 2012 by Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton. It was the winning model in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) 2012, achieving a top-5 error rate of 15.3%, which was significantly better than the second-best model's error rate of 26.2%. AlexNet consists of 5 convolutional layers, 3 max-pooling layers, and 3 fully connected layers. It uses the rectified linear unit (ReLU) activation function and dropout regularization to prevent overfitting. AlexNet was a breakthrough in deep learning and paved the way for the development of more advanced CNN architectures