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Reduces words to their root forms.
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Scales values between 0 and 1 to stabilize training.
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Compares metrics to decide which model performs best.
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Uses grammar rules to find the proper root word.
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Combines or transforms data to improve model accuracy.
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Puts trained models into apps or websites.
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Represents text by word frequency.
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Creates more samples by rotating or modifying existing ones.
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Exposes model through an API endpoint.