Explore cutting-edge neural networks for natural language processing through Stanford's comprehensive lecture series that covers fundamental concepts to advanced applications in deep learning for NLP. Begin with foundational topics including word vectors, neural classifiers, and backpropagation before progressing to syntactic structures and dependency parsing. Master recurrent neural networks, including simple RNNs and LSTM architectures, then advance to sequence-to-sequence models, attention mechanisms, and the revolutionary Transformer architecture. Delve into modern pretraining techniques, prompting strategies, and reinforcement learning from human feedback that power today's large language models. Study specialized applications including natural language generation, question answering systems, and coreference resolution while examining the theoretical connections between computational linguistics and traditional linguistic theory. Learn practical implementation skills through dedicated Python, PyTorch, and Hugging Face tutorials that demonstrate how to build and deploy NLP models. Explore emerging frontiers including code generation, multimodal deep learning that combines text with other modalities, and techniques for model interpretability and editing. Gain insights into knowledge integration in language models, model analysis and explanation methods, and current research directions shaping the future of NLP and deep learning.

Syllabus

Stanford CS224N: NLP with Deep Learning | Winter 2021 | Lecture 1 - Intro & Word Vectors
Stanford CS224N NLP with Deep Learning | Winter 2021 | Lecture 2 - Neural Classifiers
Stanford CS224N NLP with Deep Learning | Winter 2021 | Lecture 3 - Backprop and Neural Networks
Stanford CS224N - NLP w/ DL | Winter 2021 | Lecture 4 - Syntactic Structure and Dependency Parsing
Stanford CS224N - NLP w/ DL | Winter 2021 | Lecture 5 - Recurrent Neural networks (RNNs)
Stanford CS224N NLP with Deep Learning | Winter 2021 | Lecture 6 - Simple and LSTM RNNs
Stanford CS224N NLP with Deep Learning | Winter 2021 | Lecture 7 - Translation, Seq2Seq, Attention
Stanford CS224N NLP with Deep Learning | 2023 | Lecture 8 - Self-Attention and Transformers
Stanford CS224N NLP with Deep Learning | 2023 | Lecture 9 - Pretraining
Stanford CS224N | 2023 | Lecture 10 - Prompting, Reinforcement Learning from Human Feedback
Stanford CS224N NLP with Deep Learning | 2023 | Lecture 11 - Natural Language Generation
Stanford CS224N NLP with Deep Learning | Winter 2021 | Lecture 12 - Question Answering
Stanford CS224N NLP with Deep Learning | Winter 2021 | Lecture 13 - Coreference Resolution
Stanford CS224N NLP with Deep Learning | 2023 | Lecture 14 - Insights between NLP and Linguistics
Stanford CS224N NLP with Deep Learning | Winter 2021 | Lecture 15 - Add Knowledge to Language Models
Stanford CS224N NLP with Deep Learning | 2023 | Lecture 15 - Code Generation
Stanford CS224N NLP with Deep Learning | Winter 2021 | Lecture 17 - Model Analysis and Explanation
Stanford CS224N NLP with Deep Learning | Winter 2021 | Lecture 18 - Future of NLP + Deep Learning
Stanford CS224N NLP with Deep Learning | 2023 | Python Tutorial, Manasi Sharma
Stanford CS224N NLP with Deep Learning | 2023 | PyTorch Tutorial, Drew Kaul
Stanford CS224N NLP with Deep Learning | 2023 | Hugging Face Tutorial, Eric Frankel
Stanford CS224N NLP with Deep Learning | 2023 | Lecture 16 - Multimodal Deep Learning, Douwe Kiela
Stanford CS224N NLP with Deep Learning | 2023 | Lec. 19 - Model Interpretability & Editing, Been Kim