Embeddings
Embeddings Projects
Master vector embeddings for semantic search, similarity, and AI applications
Embeddings Projects
Transform text into powerful vector representations for semantic understanding
Learning Path
Projects
Beginner
| Project | Description | Time |
|---|---|---|
| Semantic Search Engine | Build semantic search with sentence-transformers | ~2 hours |
Intermediate
| Project | Description | Time |
|---|---|---|
| Text Clustering | Cluster documents using K-means and embeddings | ~3 hours |
| Similarity Recommendations | Build a recommendation system using cosine similarity | ~3 hours |
| Embedding Visualization | Visualize embeddings with t-SNE and UMAP | ~3 hours |
| Fine-tuning Embeddings | Train domain-specific embeddings | ~4 hours |
Advanced
| Project | Description | Time |
|---|---|---|
| Production Embedding Pipeline | Scalable embedding generation with caching and monitoring | ~3 days |
| Multi-Modal Embeddings | Combine text and image embeddings with CLIP | ~3 days |
| Search at Scale | Billion-scale search with FAISS and approximate nearest neighbors | ~4 days |
Why Learn Embeddings?
| Benefit | Description |
|---|---|
| Foundation | Embeddings power RAG, search, recommendations, and clustering |
| Semantic Understanding | Capture meaning beyond keyword matching |
| Versatility | Same techniques work for text, images, audio, and code |
| Efficiency | Dense vectors enable fast similarity computation |
Key Concepts
Start with the Semantic Search Engine project to learn the fundamentals.