Although deep learning models can be complex and are often called "black boxes," they can still be interpreted by using various techniques.
The interpretation is one way to make the deep learning models easier for humans to understand. It shows how these models produce and process outputs. The complexity of neural networks, and their nonlinearity, makes it difficult to interpret. It can give valuable insights into the way
they make decisions.
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