AlexNet

About

AlexNet is a pioneering deep learning model that revolutionized image recognition. Introduced in 2012 by Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton, it significantly advanced the field of computer vision. AlexNet's architecture consists of five convolutional layers followed by three fully connected layers, leveraging GPUs for faster computation. It introduced key innovations like ReLU activation functions and overlapping pooling, which improved training speed and accuracy. AlexNet's success in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) marked a milestone in deep learning, demonstrating its potential to handle large datasets. Its impact extends beyond image classification, influencing various applications in autonomous vehicles, medical imaging, and more. Despite its large size and computational requirements, AlexNet remains a foundational model in deep learning, inspiring subsequent architectures and techniques. Its contributions have been instrumental in the rapid progress of AI and machine learning.