Lilly Tech Systems
Learn Embeddings
Master the foundation of modern AI — how text, images, and data are converted to numerical vectors that capture semantic meaning. From Word2Vec to state-of-the-art embedding models.
Your Learning Path
Follow these lessons in order, or jump to any topic that interests you.
1. Introduction
What are embeddings? The bridge between human language and mathematics. Words as points in space.
2. How Embeddings Work
Transformer architectures, training objectives, dimensionality, and visualizing embeddings.
3. Text Embeddings
Create embeddings with OpenAI, Voyage AI, Cohere, Google, and open-source models.
4. Embedding Models
Compare models: dimensions, pricing, quality benchmarks, and choosing the right one.
5. Practical Applications
Semantic search, recommendations, clustering, duplicate detection, and RAG pipelines.
6. Fine-tuning Embeddings
Train domain-specific embedding models with contrastive learning and triplet loss.
7. Best Practices
Dimensions, preprocessing, long documents, caching, monitoring, and common mistakes.
What You'll Learn
By the end of this course, you'll be able to:
Understand Embeddings
Know how embedding models convert text and data into meaningful numerical vectors.
Use Embedding APIs
Generate embeddings with OpenAI, Voyage AI, Cohere, Google, and sentence-transformers.
Build AI Applications
Implement semantic search, recommendations, clustering, and RAG with embeddings.
Fine-tune Models
Train custom embedding models for domain-specific tasks and evaluate their quality.