Signing you in…

Embeddings: from sparse tokens to dense meaning

Chapter 6: NLP — from embeddings to LLMs

Natural language is the hardest kind of data: text has no native numeric structure, words depend on context, and meaning is defined by the whole sequence. This chapter walks from the start: how to turn text into numbers (embeddings), how attention lets a model see full context at once (Transformer), how BERT differs from GPT, how RAG works, and why LoRA makes fine-tuning practical. PyTorch + Hugging Face are the hands-on stack.

Content is available with subscription.
Get full access to all courses on the platform for one year with a single payment.
Unlike other platforms that charge per course, here you get everything for one price, and after one year of use there will be no automatic charge for the following year.