Gradient-Based Learning Applied to Document Recognition, Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner, 1998Proceedings of the IEEE, Vol. 86 (IEEE)DOI: 10.1109/5.726791 - This paper introduced LeNet-5, a convolutional network architecture that established many foundational concepts of CNNs, including convolution, pooling, and parameter sharing for image recognition.
Deep Learning, Ian Goodfellow, Yoshua Bengio, and Aaron Courville, 2016 (MIT Press) - Chapter 9 provides a detailed theoretical background on convolutional networks, covering the principles of convolution, pooling, and various architectural considerations.
CS231n: Convolutional Neural Networks for Visual Recognition, Fei-Fei Li, Justin Johnson, Serena Yeung, and Richard Zhang, 2023 (Stanford University) - Comprehensive online course notes offering an accessible explanation of CNN architecture, convolution, pooling, and practical considerations for image tasks.
Build the Neural Network, PyTorch Documentation Team, 2021 (PyTorch) - Official PyTorch tutorial demonstrating how to construct a convolutional neural network using nn.Conv2d, nn.MaxPool2d, and other core PyTorch modules.