CS224n: Natural Language Processing with Deep Learning
4.9(7,000)·1.2M enrolled
Advanced 50 hours English None CertificateFREE
Editor's Pick
Manning's CS224n is where engineers go to actually understand how LLMs work — not apply them, but understand them. Essential depth.
About this course
CS224n is Stanford's NLP with deep learning course and arguably the most important academic resource for understanding how modern language models work. Taught by Christopher Manning — one of the foremost NLP researchers in the world — it covers word vectors (Word2Vec, GloVe), recurrent neural networks, sequence-to-sequence models, attention mechanisms, the Transformer architecture that underpins every modern LLM, BERT, GPT, and the current landscape of large language models.
At a time when every company wants engineers who 'understand LLMs,' CS224n is the course that actually teaches the foundations — how attention works mathematically, why transformers replaced RNNs, and what BERT and GPT are actually doing. It's the academic complement to applied LLM courses in this catalog (LangChain, OpenAI API) that take the models as given.
What you'll learn
Understand word vectors and their mathematical foundations
Implement and understand recurrent neural networks for sequence tasks
Build and understand the Transformer architecture from first principles
Understand BERT, GPT, and how pre-training and fine-tuning work
Apply NLP models to tasks including classification, translation, and QA
This course includes
50h
On-demand video
Yes
Mobile access
English
Language
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CS224n: Natural Language Processing with Deep Learning
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Instructor
CM
Christopher Manning
Stanford Online instructor
1.2M+ learners5 courses4.9 instructor rating
Taught by Christopher Manning, Thomas M. Siebel Professor in Machine Learning at Stanford and co-author of foundational NLP textbooks including 'Foundations of Statistical Natural Language Processing.'
Requirements
Solid Python and NumPy skills
Understanding of basic neural networks and backpropagation
Linear algebra and probability comfort
Who this course is for
ML engineers and researchers who want to understand how LLMs actually work
Data scientists working with NLP and text data at a technical level
Anyone who applies LLMs and wants the mathematical foundations behind them
About this provider
SO
Stanford Online
Stanford University's online learning platform offering free and paid courses from Stanford faculty across AI, ML, medicine, and computer science.
The foundations it teaches (transformers, attention, pre-training) are what all current LLMs are built on. The specific models advance faster than any course can track, but the architecture coverage gives you the tools to understand new models as they're released.
The lecture videos and slides are available free on Stanford Online and YouTube; assignments are shared publicly. Some offerings include paid certificate options.
CS224n teaches the foundations — how the models work mathematically. Applied courses (LangChain, OpenAI API, HuggingFace) teach how to use the models in production. Both are valuable and serve different depths.