AlexNet is a pioneering deep convolutional neural network architecture that revolutionized the field of computer vision and image recognition. Proposed by Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton in 2012, it won the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) with a significant margin, drastically outperforming previous approaches. AlexNet consists of eight layers, including five convolutional layers and three fully connected layers. It introduced several groundbreaking concepts, such as the use of Rectified Linear Units (ReLU) as activation functions, overlapping pooling layers, and the innovative concept of Dropout to prevent overfitting. AlexNet’s success highlighted the potential of deep learning and paved the way for the development of more complex and efficient neural network architectures. Its impact can be seen in various real-world applications, from image recognition to natural language processing, and it has inspired numerous advancements in the field of artificial intelligence.

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