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Learn the fundamentals and advanced concepts of natural language processing in this comprehensive course covering both traditional and modern approaches to NLP. Master foundational machine learning techniques including logistic regression and feedforward neural networks before progressing to neural classification with word embeddings and vector semantics. Explore essential NLP tasks such as tokenization, morphology, part-of-speech tagging, named entity recognition, and dependency parsing using Hidden Markov Models and the CKY algorithm. Dive deep into language modeling, machine translation with sequence-to-sequence models, attention mechanisms, and BLEU evaluation metrics. Study the Transformer architecture and its practical applications, including pretraining and finetuning strategies for models like DeBERTa. Examine advanced topics in large language models including prompting techniques, parameter-efficient finetuning with LoRA and QLoRA, reinforcement learning from human feedback (RLHF), and retrieval-augmented generation. Investigate specialized applications such as question answering systems, abstractive and extractive summarization, multilingual LLMs, and vision-and-language models. Benefit from guest lectures by industry experts covering cutting-edge topics like data curation for pretrained language models, LLM quantization techniques for improved efficiency, and privacy considerations in large language models. Gain hands-on experience through practical demonstrations and tutorials using PyTorch, preparing you to tackle real-world NLP challenges with both traditional and state-of-the-art deep learning approaches.
