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4. Easy Things Are Hard: The Deep Learning Revolution in Computer Vision
This text explains the challenges of computer vision and the success of convolutional neural networks (ConvNets) in overcoming them. It traces the history of computer vision research, highlighting early failures and the subsequent revolution brought about by deep learning. The author uses the example of distinguishing dogs and cats in images to illustrate
how ConvNets, inspired by the hierarchical structure of the brain's
visual cortex, learn to recognize objects through layers of increasingly
complex feature detection. Finally, the text describes the training process of ConvNets using backpropagation and the importance of large datasets.
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By Chris GuoThis text explains the challenges of computer vision and the success of convolutional neural networks (ConvNets) in overcoming them. It traces the history of computer vision research, highlighting early failures and the subsequent revolution brought about by deep learning. The author uses the example of distinguishing dogs and cats in images to illustrate
how ConvNets, inspired by the hierarchical structure of the brain's
visual cortex, learn to recognize objects through layers of increasingly
complex feature detection. Finally, the text describes the training process of ConvNets using backpropagation and the importance of large datasets.