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Differentiate between bias and variance in the context of deep learning models. How can you achieve balance between the two?

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Comprehending prediction errors is crucial when it comes to understanding predictions. Reducible (errors that arise due to squared bias or squared variance) and irreducible (errors that arise due to the randomness or natural variability in a system and cannot be reduced by varying the model) mistakes are the two primary types of errors. There are two types of reducible errors: bias and variance. Gaining a thorough grasp of these flaws aids in the construction of an accurate model by preventing overfitting and underfitting.

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