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Core
CV Architectures: ResNets, ViT, Detection
Modern computer vision stands on three pillars: residual connections that let CNNs reach hundreds of layers deep without degrading, Vision Transformers that patchify an image and run self-attention in place of convolutions, and detection heads (one-stage vs two-stage) scored by mAP after non-maximum suppression. AI, ML, and GenAI engineer interviews probe this to check that you can pick an architecture, fine-tune a pretrained backbone, and reason about latency vs accuracy rather than train from scratch.
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