Introduction to Large Language Models (LLMs)
• Overview of LLM concepts, transformer architectures, and NLP fundamentals
• Applications across conversational AI, summarization, information retrieval, and content generation
Core LLM Components
• Transformer models such as GPT, BERT, LLaMA, and T5
• Training, fine-tuning, transfer learning, and prompt engineering
• Tokenization, embeddings, attention mechanisms, and generation techniques
LLM Platforms & Development
• Hands-on with TensorFlow, PyTorch, Hugging Face Transformers, and LangChain
• Building training and evaluation pipelines for large-scale NLP models
• Data preprocessing, dataset management, and model optimization workflows