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Convolutional Neural Networks - Xavier Giro - UPC TelecomBCN Barcelona 2020
The document provides an overview of convolutional neural networks (CNNs), detailing their architecture, interpretability, computation, receptive fields, and applications. It discusses the historical evolution of CNNs, key architectures such as LeNet and AlexNet, and the advantages of applying CNNs to various data types, including images, text, and speech. Additionally, it highlights techniques for improving accuracy and computational efficiency in training deep learning models.
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