Understanding the AlexNet Architecture - Delhi

Alexnet architecture is a groundbreaking convolutional neural network (CNN) architecture introduced in 2012 by Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton. It revolutionized deep learning by significantly improving image classification tasks, winning the ImageNet Large Scale Visual Recognition Challenge (ILSVRC). AlexNet consists of eight layers: five convolutional layers for feature extraction and three fully connected layers for classification. Key innovations include ReLU activation functions, dropout for regularization, and overlapping max-pooling. The architecture also leverages GPU parallelism for efficient training on large datasets. AlexNet's success paved the way for modern deep-learning research in computer vision and beyond.