Mathematics for NLP
Natural Language Processing might seem like a field focused mainly on text and language, but beneath the surface, it is powered by mathematics. To build systems that understand, analyze, and generate human language, we need to represent words, sentences, and documents using numbers. These numerical representations are then used in algorithms that rely on mathematical concepts from areas such as linear algebra, probability theory, statistics, and calculus.
This section introduces the key mathematical foundations that support modern NLP. You don’t need to be an expert in advanced math to understand these ideas, but having a basic understanding will help you grasp how NLP models work, why certain algorithms behave the way they do, and how to evaluate their performance.
We will begin by exploring how language can be transformed into vectors using methods like one-hot encoding and word embeddings. Then we will look at how linear algebra is used to calculate distances and similarities between words. Finally, we will examine probability and statistics, which are essential for tasks such as language modeling, classification, and machine translation.
By the end of this section, you will have a solid understanding of the mathematical tools that make NLP possible — and you’ll be better prepared to design, improve, and analyze language-based AI systems.
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