Mathematics for NLP

Table of Contents: Mathematics for NLP

1. Introduction to Mathematics for NLP

• Why Mathematics is Essential for NLP

• Overview of Mathematical Concepts in NLP

2. Linear Algebra in NLP

• Singular Value Decomposition (SVD)

• Word Embeddings (Word2Vec, GloVe)

• Principal Component Analysis (PCA) for Dimensionality Reduction

3. Probability and Statistics

• Language Modeling (e.g., n-grams)

• Hidden Markov Models (HMMs)

• Probabilistic Topic Modeling (e.g., Latent Dirichlet Allocation)

4. Calculus for NLP

• Neural Network Training

• Optimization in Embedding Models

5. Information Theory

• Perplexity in Language Models

• Model Evaluation Metrics

• Text Compression

6. Graph Theory and Networks

• Knowledge Graphs

• Semantic Role Labeling

7. Optimization Techniques

• Fine-Tuning Language Models

• Loss Function Minimization

8. Discrete Mathematics and Combinatorics

• Parsing and Syntax Analysis

• Text Generation

9. Linear Programming and Integer Programming

• Coreference Resolution

• Text Summarization

10. Tensor Mathematics for NLP

• Transformer Models (e.g., Self-Attention Mechanism)

• Deep Learning Architectures

11. Neural Networks and Deep Learning Mathematics

• Sentiment Analysis

• Machine Translation

12. Case Studies and Applications

• Mathematical Techniques in Modern NLP Models

• Mathematics Behind Transformer Architectures (e.g., BERT, GPT)

• Real-World Use Cases of Mathematics in NLP