Glossary › Embedding

Embedding

A dense vector representation of a discrete entity (word, user, product, category). Learned from data such that similar entities are close in vector space. Embeddings reduce dimensionality compared to one-hot encoding and allow the model to generalize. The embedding layer is usually the first layer in models that handle categorical inputs.

← Early Stopping · F1 Score → · All terms

Where Embedding shows up

All matching posts

NLP Engineering: From Text to Production

NLP has transformed with the rise of transformers, but the engineering fundamentals remain: preprocessing, embeddings, fine-tuning, and serving. Here is the full practical stack.

#nlp#transformers#huggingface#deployment

Embeddings: What They Are and Why They Work

Embeddings are the foundation of modern NLP and recommendation systems. This post explains the core idea, why it works, and how to use them in practice.

#embeddings#vector-search#deep-learning

Building a RAG System From Scratch

A complete walkthrough of building a production-ready RAG system: document ingestion, chunking, embedding, vector search, reranking, and generation with citation.

#rag#vector-search#embeddings#python