AlexNet exerted a significant influence on contemporary deep learning. In 2012, Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton created a groundbreaking method for picture identification tasks. Their approach utilized deep convolutional neural networks (CNNs), leading to a significant advancement in this field. AlexNet showcased the substantial enhancement in accuracy that deep learning might bring to computer vision tasks. The model achieved a significant victory in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), surpassing the second-place entry by a wide margin. It had a top-5 error rate of 15.3%, while the second-place entry had a rate of 26.2%. This achievement made the use of GPUs for training deep networks more widely accepted and motivated the creation of more sophisticated architectures such as VGG, ResNet, and Inception. The impact of AlexNet can be observed in its influence on many applications, including as facial recognition, autonomous driving, and medical picture analysis.