AdvancedVector Embeddings & Semantic SearchEnglish12 pages
Embeddings, Vector Search & Vector Databases – Handwritten Notes
Learn embeddings, vector search and vector databases through clear handwritten notes. Understand similarity metrics, HNSW, IVF, hybrid search, reranking and production retrieval workflows. Ideal for AI Engineers, RAG Developers and LLM learners.
What you will learn
- ✓Understand embeddings and numerical vector representations.
- ✓Learn cosine similarity, dot product and Euclidean distance.
- ✓Understand embedding models, dimensions and normalization.
- ✓Build document embedding and vector search pipelines.
- ✓Learn vector databases, metadata and filtering.
- ✓Understand exact search and approximate nearest neighbor search.
- ✓Explore HNSW and IVF indexing techniques.
- ✓Implement practical embedding workflows using Python.
- ✓Understand hybrid search using dense and keyword retrieval.
- ✓Learn BM25, reranking and Reciprocal Rank Fusion (RRF).
- ✓Evaluate retrieval using Precision@K, Recall@K and latency.
- ✓Identify common retrieval failures and embedding quality issues.
- ✓Understand vector search optimization, security and production monitoring.
#Embeddings#Vector Search#Vector Database#Semantic Search#Cosine Similarity#Dot Product#Euclidean Distance#Embedding Models#Vector Indexing#HNSW#IVF#Approximate Nearest Neighbor#ANN Search#Hybrid Search#BM25#Reranking#Reciprocal Rank Fusion#RAG#Retrieval Evaluation#AI Engineering
Licence
Total₹129
No account needed · Manual UPI payment · PDF emailed after verification
Details
- Pages
- 12
- Category
- Embeddings, Vector Search & Vector Databases
- Subject
- Vector Embeddings & Semantic Search
- Format
- PDF (watermarked)