Fine-tuning adapts pre-trained models to specific tasks by continuing training on smaller, task-specific datasets.
Fine-tuning adapts pre-trained models to specific tasks by continuing training on smaller, task-specific datasets.
Neural Networks mimic the human brain's structure, using layers of interconnected nodes (neurons) to learn patterns from data.
Transformers revolutionized NLP by using self-attention mechanisms, allowing models to weigh the importance of different words in a sequence.
Prompt Engineering is the art of crafting inputs that get the best responses from LLMs - it's like programming but with natural language.
RAG (Retrieval Augmented Generation) combines LLMs with external knowledge bases to reduce hallucinations and improve accuracy.