Deep learning has revolutionized the field of artificial intelligence, enabling machines to perform complex tasks with high accuracy. In this section, we will explore some of the key papers in the field of deep learning.
Key Papers in Deep Learning
AlexNet (2012): This paper introduces the concept of using deep convolutional neural networks for image recognition. It won the ImageNet competition and kick-started the deep learning revolution.
VGGNet (2014): The VGGNet paper proposes a simple and efficient architecture for image classification, which has been widely used in various computer vision tasks.
GoogLeNet (2015): This paper introduces the Inception architecture, which significantly improves the performance of deep neural networks on image classification tasks.
ResNet (2015): The ResNet paper introduces the concept of residual learning, which allows for the training of very deep networks without the vanishing gradient problem.