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Understand how vector databases work under the hood, when to use them, and how to choose between Pinecone, Weaviate, ChromaDB, and Qdrant for your application.

Learn how Named Entity Recognition works, when to use different approaches, and how to train custom models for domain-specific entities.

A guide to text embeddings from Word2Vec to Sentence Transformers—how they work, when to use each type, and practical implementation patterns.

Deep dive into reward modeling - the critical first step in RLHF that teaches AI systems to predict and optimize for human preferences through comparative learning and preference ranking.

Comprehensive guide to supervised fine-tuning of Large Language Models, covering data preparation, training implementation, hyperparameter optimization, and evaluation strategies with practical code examples.