Explore the fundamentals and advanced concepts of natural language processing through this comprehensive lecture series from Stanford University's CS224N course. Begin with foundational topics including word vectors, neural classifiers, and backpropagation before progressing to more sophisticated architectures like recurrent neural networks, LSTMs, and the revolutionary Transformer model. Master key NLP tasks such as dependency parsing, machine translation, sequence-to-sequence modeling, and attention mechanisms. Delve into cutting-edge developments including self-attention, pretraining strategies, question answering systems, natural language generation, and coreference resolution. Examine large language models like T5, explore methods for incorporating knowledge into language models, and understand the social and ethical implications of NLP technology. Learn practical approaches to model analysis and explanation while gaining insights into the future directions of NLP and deep learning research. The series also includes specialized guest lectures covering low-resource machine translation, BERT and other pretrained models, socially intelligent NLP systems, knowledge representation building, and scaling language models, providing a well-rounded understanding of both theoretical foundations and real-world applications in modern natural language processing.

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 | Winter 2021 | Lecture 8 - Final Projects; Practical Tips
Stanford CS224N NLP with Deep Learning | Winter 2021 | Lecture 9 - Self- Attention and Transformers
Stanford CS224N NLP with Deep Learning | Winter 2021 | Lecture 10 - Transformers and Pretraining
Stanford CS224N NLP with Deep Learning | Winter 2021 | Lecture 12 - Question Answering
Stanford CS224N NLP with Deep Learning | Winter 2021 | Lecture 12 - Natural Language Generation
Stanford CS224N NLP with Deep Learning | Winter 2021 | Lecture 13 - Coreference Resolution
Stanford CS224N NLP with Deep Learning | Winter 2021 | Lecture 14 - T5 and Large Language Models
Stanford CS224N NLP with Deep Learning | Winter 2021 | Lecture 15 - Add Knowledge to Language Models
Stanford CS224N NLP with Deep Learning | Winter 2021 | Lecture 16 - Social & Ethical Considerations
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 | Winter 2020 | Low Resource Machine Translation
Stanford CS224N: NLP with Deep Learning | Winter 2020 | BERT and Other Pre-trained Language Models
Stanford CS224N I NLP with Deep Learning | Spring 2022 | Socially Intelligent NLP Systems
Stanford CS224N NLP with Deep Learning |Spring 2022|Guest Lecture: Building Knowledge Representation
Stanford CS224N NLP with Deep Learning | Spring 2022 | Guest Lecture: Scaling Language Models