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End-to-end layers refer to the complete pipeline in a machine learning model, from input to output, where each layer processes data sequentially. This approach enables the model to learn complex patterns directly from raw data, reducing the need for manual feature engineering. In deep learning, end-to-end systems typically involve multiple layers of neural networks that transform the input data progressively until a final prediction or classification is made. The goal is to optimize the entire process as a single unit, improving efficiency and performance.

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Related Questions

Which layer manages the end to end control and error checking?

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