LeNet: Gradient-Based Learning Applied to Document Recognition

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The network stacks convolutional layers that apply the same small learned filters across the image with pooling layers that reduce resolution, so it detects local patterns regardless of position while keeping the parameter count low through weight sharing. Trained on labeled digit images, it learns its own feature detectors instead of relying on manually designed ones. It achieved strong accuracy on handwritten digit recognition and was deployed to read amounts on bank checks, demonstrating a practical gradient-trained vision system.