|
LO-1
|
Upon successful completion of this course, students will analyze the historical development of large language models and NLP fundamentals, deeply investigate the Transformer architecture, attention mechanisms, and encoder-decoder structures, evaluate model pre-training, fine-tuning, and reinforcement learning from human feedback (RLHF) processes, design integrated RAG systems with vector databases by applying advanced prompt engineering techniques like chain-of-thought and in-context learning, perform model optimization using LoRA and quantization methods, and generate solutions for ethical and security issues such as bias, hallucination, and data privacy in AI systems. |