Part 21: Generative AI Fundamentals - LLMs, Embeddings & Vector Spaces
Understand transformer architecture, tokenization, temperature, embeddings, cosine similarity, and vector math behind large language models.
#Machine Learning
Career Guide Career Guide Part 22: Vector Databases - Pinecone, Chroma, Milvus & PGVector
Learn index types (HNSW, IVF), metadata filtering, hybrid search, performance tuning, and managed vs self-hosted vector databases.
Career Guide Part 23: RAG Architectures - Semantic Retrieval & Knowledge Systems
Master chunking strategies, embedding models, re-ranking (Cohere), hybrid search, contextual compression, and evaluation frameworks for RAG systems.