Learn how Transfer Learning and Domain Adaptation power modern Large Language Models (LLMs) like GPT and BERT! This video explains how LLMs reuse general knowledge to tackle specific tasks or domains efficiently, saving time, data, and computational resources.

We cover:
- Transfer Learning: Pre-training on large datasets and fine-tuning for tasks like sentiment analysis, chatbots, content generation, medical research, and code development.
- Domain Adaptation: Specialized techniques to adapt models to new domains, including continued pre-training (CPT), supervised fine-tuning (SFT), retrieval-augmented generation (RAG), and parameter-efficient fine-tuning (PEFT).
- Key challenges like data scarcity, domain shift, and catastrophic forgetting.
- Comparison: Transfer Learning vs. Domain Adaptation — when to use each.

Perfect for AI enthusiasts, data scientists, and NLP learners who want to understand how LLMs are trained, specialized, and applied across diverse domains

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