{"id":12145,"date":"2026-08-10T10:41:41","date_gmt":"2026-08-10T10:41:41","guid":{"rendered":"https:\/\/nokobox.com\/index.php\/item\/udemy-llms-foundations-tokenization-and-word-embeddings-models-2025-9\/"},"modified":"2026-08-10T10:41:41","modified_gmt":"2026-08-10T10:41:41","slug":"udemy-llms-foundations-tokenization-and-word-embeddings-models-2025-9","status":"publish","type":"digital_item","link":"https:\/\/nokobox.com\/index.php\/item\/udemy-llms-foundations-tokenization-and-word-embeddings-models-2025-9\/","title":{"rendered":"Udemy \u2013 LLMs Foundations: Tokenization and Word Embeddings Models 2025-9"},"content":{"rendered":"
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Description<\/span><\/h2>\n

LLMs Foundations: Tokenization and Word Embeddings Models. This course is designed to provide a comprehensive and practical understanding of the fundamental concepts of language models such as tokenization and word embeddings. This course, suitable for beginners, examines in a clear and practical way two fundamental parts of the architecture of these models. Over six hours of video content, participants will learn the process of converting raw text into machine-processable units (tokenization) and how to represent the meaning of words in multidimensional space (word embeddings). Using PyTorch, learners will also be able to develop their own word embedding models, apply them to practical applications such as question answering systems, and even implement a small language model from scratch. By breaking down complex mathematical concepts into digestible parts, this course teaches not only how to use the techniques, but also the underlying rationale for each method. By the end of the course, participants will have a solid foundation in the mechanisms of large language models and the confidence to use these skills in real-world AI projects.<\/span><\/p>\n

What you will learn<\/span><\/h3>\n