Briefly introduce the AlexNet model

AlexNet is a deep convolutional neural network architecture that was introduced by Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton in 2012. It was the winning model of the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) in 2012, which is an annual competition for computer vision models. AlexNet consists of eight layers, including five convolutional layers and three fully connected layers. It also uses techniques such as ReLU activation, local response normalization, and dropout to improve the model's performance and reduce overfitting. AlexNet's success marked a turning point in the field of computer vision, demonstrating the power of deep learning and paving the way for more advanced models