Embeddings Deep Dive Guide

Lecciones

Módulos

🎓

Acceso por bootcamp

Lo que aprenderás

✓Understand embeddings architecture: transformers, tokenization, pooling, normalization

✓Compare embedding models objectively: OpenAI, SBERT, BGE with MTEB benchmarks

✓Implement semantic search from scratch with numpy (no black-box libraries)

✓Apply chunking strategies: fixed-size, semantic, recursive — and evaluate which works best

✓Master distance metrics: cosine similarity, euclidean, dot product — know when to use each

✓Perform embedding operations: arithmetic, interpolation, composition, clustering

✓Optimize for production: caching, error handling, monitoring, scaling, cost control

✓Evaluate embedding quality with golden datasets, precision@k, recall@k, MRR

✓Build a complete semantic search engine with FAISS, query expansion and reranking

•AI Engineers implementing semantic search or RAG who need deep embedding understanding
•Backend developers transitioning to AI who want production-ready skills from day one
•Professionals who use `langchain.embeddings` but can't debug when things break
•Teams choosing embedding models who need an objective comparison framework
•Students in the AI Engineering Path preparing for vector databases and advanced RAG

•Python basics + OOP (loops, functions, classes)
•REST APIs and HTTP fundamentals (requests, JSON)
•OpenAI API access (or open-source alternative)
•Vector concepts from AI Semantics Guide (#5) — what vectors are, cosine similarity, semantic search concept
•Basic numpy (arrays, operations)

1Módulo 1: ¿Qué son Embeddings?8 lecciones

2Módulo 2: ¿Cómo funcionan Embeddings?8 lecciones

3Módulo 3: Modelos de Embeddings (Comparación)8 lecciones

4Módulo 4: Evaluación y Chunking Strategies8 lecciones

5Módulo 5: Distance Metrics Profundo8 lecciones

6Módulo 6: Embedding Operations8 lecciones

7Módulo 7: Production Patterns8 lecciones

8Módulo 8: Proyecto Integrador8 lecciones

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