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: Deep features are typically output as numerical vectors (a row of numbers) from the last fully connected or pooling layer before the final classification. Common Applications

detect simple patterns like edges, textures, or blobs. Intermediate layers combine these into more complex shapes. 78E0C7C5-B8A7-4FE7-A739-9592B5DB499F.jpeg

represent high-level concepts or objects (e.g., a "wheel" or a "face"). : Deep features are typically output as numerical