8 articles
Build a production semantic search engine using OpenAI embeddings, cosine similarity, and vector databases. Complete Python guide with real-world examples, performance optimization, and deployment patterns.
Master HuggingFace Transformers in 2026: text classification, NER, summarization, translation, question answering, image classification, and audio — with production deployment patterns.
Understand embeddings and semantic search: from word vectors to meaning-based retrieval.
Generate embeddings locally using Sentence Transformers without API calls.
Master Hugging Face Transformers library for loading, fine-tuning, and deploying models.
Compare zero-shot, few-shot, embedding-based, and fine-tuned classification approaches with production trade-offs.
Build production DPR systems: train dual encoders, fine-tune on domain data, scale with FAISS, and outperform BM25 on specialized domains.
Transform user queries to improve retrieval with rewriting, HyDE, step-back prompting, and multi-hop decomposition techniques that boost RAG accuracy.