why CNN is preferred over feed forward neural networks ?

Question

Accepted Answer

CNN is preferred over feed forward neural networks due to their ability to capture spatial and temporal dependencies in data.

CNNs are designed to effectively capture spatial relationships in data, making them ideal for tasks like image recognition.
CNNs use shared weights and local connectivity to efficiently learn patterns in data, reducing the number of parameters compared to feed forward neural networks.
CNNs are also capable of capturing temporal dependencies in sequential data, such as in natural language processing tasks.
CNNs have shown superior performance in various tasks like image classification, object detection, and speech recognition.
CNNs are more robust to variations in input data, making them suitable for real-world applications where data may be noisy or incomplete.

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