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A neural network architecture built around convolution layers that share weights across spatial positions. CNNs were the dominant approach for image recognition before transformer-based vision models, and they still appear in many production systems where compute budgets are tight. The shared filters give CNNs translation invariance for visual patterns.
Computer vision and edge-device AI work still leans heavily on CNNs. Knowing when a smaller convolutional model beats a transformer is part of practical AI engineering.
A neural network architecture built around convolution layers that share weights across spatial positions. CNNs were the dominant approach for image recognition before transformer-based vision models, and they still appear in many production systems where compute budgets are tight. The shared filters give CNNs translation invariance for visual patterns.
Computer vision and edge-device AI work still leans heavily on CNNs. Knowing when a smaller convolutional model beats a transformer is part of practical AI engineering.
Definitions are original explanations written for career development purposes. For authoritative technical definitions, refer to NIST, ISO, or the relevant standards body.
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