Reference 4 stops to get here

TF-IDF

Term Frequency-Inverse Document Frequency - a statistical measure of word importance in documents, used for information retrieval.

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4 stops · basics first
  1. Token ✓ understood

    The basic unit of text that a language model processes, typically representing a word, subword, or character. Tokens are the fundamental building blocks for LLM input and output.

  2. Tokenization ✓ understood

    Splitting text into tokens, usually subword pieces, and mapping each to an integer ID so a language model can process it.

  3. Dataset ✓ understood

    A collection of data examples used for training, validating, or testing machine learning models.

  4. Feature ✓ understood

    A single measurable property of an example, such as a house's floor area or how many links an email contains, used as an input to a model.

  5. TF-IDF · you are here ✓ understood

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Tokenization Feature
TF-IDF

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Language & LLMs N-gram A contiguous sequence of n items (words, characters) from text, used in language modeling and feature extraction. Language & LLMs Stop Words Common words (the, is, at) often removed in NLP preprocessing as they carry little semantic meaning. Language & LLMs Text Classification Assigning categories or labels to text documents, a fundamental NLP task. Language & LLMs Topic Modeling Discovering abstract topics in document collections, often using techniques like LDA. Foundations One-Hot Encoding Converting categorical variables into binary vectors with one element set to 1 and others to 0.