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Explore the fundamentals and advanced concepts of natural language processing through this comprehensive university-level course covering machine learning foundations, neural networks, word embeddings, language modeling, attention mechanisms, transformers, and modern large language model techniques. Begin with essential machine learning and neural network principles before progressing through word2vec skip-gram models, sequence-to-sequence architectures for neural machine translation, and subword tokenization methods. Master transformer architectures in depth across two dedicated modules, then advance to contemporary topics including pretraining and finetuning strategies, retrieval-augmented generation (RAG), question answering systems, text generation evaluation, and instruction finetuning with supervised fine-tuning (SFT). Delve into cutting-edge techniques such as reinforcement learning from human feedback (RLHF), direct preference optimization (DPO), quantization methods, key-value caching, and parameter-efficient fine-tuning approaches like (Q)LoRA for optimizing large language models.
