{"id":14778,"date":"2026-08-10T11:18:47","date_gmt":"2026-08-10T11:18:47","guid":{"rendered":"https:\/\/nokobox.com\/index.php\/item\/udemy-the-math-of-large-language-models-transformer-architectures-2026-6\/"},"modified":"2026-08-10T11:18:47","modified_gmt":"2026-08-10T11:18:47","slug":"udemy-the-math-of-large-language-models-transformer-architectures-2026-6","status":"publish","type":"digital_item","link":"https:\/\/nokobox.com\/index.php\/item\/udemy-the-math-of-large-language-models-transformer-architectures-2026-6\/","title":{"rendered":"Udemy \u2013 The Math of Large Language Models: Transformer Architectures 2026-6"},"content":{"rendered":"
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Description<\/h2>\n

The Math of Large Language Models: Transformer Architectures is a course on the mathematical foundations behind modern large language models (LLMs) and transformer-based neural networks published by Udemy Online Academy. This course provides a rigorous yet practical exploration of the mathematical foundations behind modern large language models (LLMs) and transformer-based neural networks. Designed for AI engineers, machine learning specialists, data scientists, and students, this course explains the core mathematical concepts that enable transformer models to understand and produce human language.<\/p>\n

Students will study linear algebra, probability, calculus, vector embeddings, attention mechanisms, positional encoding, normalization, optimization, and gradient-based learning, while learning how these concepts are applied to transformer architectures. By the end of this course, you will have a clear mathematical understanding of the core ideas that underpin today\u2019s large language models, enabling you to confidently understand, analyze, and discuss transformer architectures without relying on programming frameworks or specific implementation details.<\/p>\n

What you will learn in The Math of Large Language Models: Transformer Architectures:<\/h3>\n