AlexNet had a major impact on modern deep learning. Developed by Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton in 2012, it revolutionized image recognition tasks by using deep convolutional neural networks (CNNs). AlexNet demonstrated that deep learning could significantly improve accuracy in computer vision tasks. It won the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) by a large margin, achieving a top-5 error rate of 15.3% compared to the second-place entry’s 26.2%. This success popularized the use of GPUs for training deep networks and inspired the development of more advanced architectures like VGG, ResNet, and Inception. AlexNet’s impact can be seen in its influence on various applications, including facial recognition, autonomous driving, and medical image analysis.