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Book Overview & Buying
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Table Of Contents
LLMs in Enterprise
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Data standardization involves rescaling the features so that they have a mean of zero and a standard deviation of one. This ensures that features are on the same scale, making it easier for models to learn patterns without being biased toward certain features. On the other hand, normalization typically rescales features to a range, such as [0, 1], which is useful when features have different units or magnitudes. Both techniques help prevent some features from dominating others during training and contribute to improved model performance.
One of the key components of preparing data for LLMs is tokenization. Tokenization refers to breaking down text into smaller units, such as words or subwords, which can then be processed by the model. Different tokenization techniques are used depending on the nature of the model and the language being processed. For example, BERT uses a WordPiece tokenizer, which splits words...