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  • THEORY
    • Math Basics
      • Chapter 2 Groups, Rings & Fields
        • 2.1 Groups, Subgroups, Cosets
    • Information Theory
      • Chapter 2 Entropy, Relative Entropy & Mutual Information
    • Formal Languages
      • Regular Expressions
    • Language Processing
      • Chapter 2 RE, Text Norm, Edit dist.
        • 2.2 Words
        • 2.3 Corpora
        • 2.4 Text Normalization
        • 2.5 Minimum Edit Distance
      • Chapter 3 N-gram Language Models
        • 3.1 N-Grams
        • 3.2 Evaluating Language Models
        • 3.3 Generalization and Zeros
        • 3.4 Smoothing
        • 3.5 Kneser-Ney Smoothing
      • Chapter 4 Naive Bayes and Sentiment Classification
        • 4.1 Naive Bayes Classifiers
        • 4.6 Naive Bayes as a Language Model
        • 4.7 Evaluation: Precision, Recall, F-measure
      • Chapter 5 Logistic Regression
        • 5.3 The Cross-entropy Loss Function
      • Chapter 6 Vector Semantics and Embeddings
        • 6.1 Lexical Semantics
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  • TOOLS - GRAPHICAL
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  • TOOLS - NUMERICAL
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