AlexNet Convolutional Neural Network

AlexNet is a deep convolutional neural network (CNN) proposed by Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton in 2012. It achieved breakthrough results in the ImageNet image classification competition that year and won first place in the 2012 ILSVRC competition, leading the revival of deep learning in the field of image recognition.ImageNet Classification with Deep Convolutional Neural Networks", and published in the NIPS 2012 conference.

AlexNet has a relatively deep network structure, including 5 convolutional layers and 3 fully connected layers, with 60 million parameters and 650,000 neurons. It uses a series of innovative technologies, such as using the ReLU activation function to solve the gradient vanishing problem that may occur in the traditional Sigmoid activation function, making the network converge faster and enhancing nonlinear expression capabilities.