Stanford CS 224N | Natural Language Processing with Deep Learning (original) (raw)

Natural language processing (NLP) is a crucial part of artificial intelligence (AI), modeling how people share information. In recent years, deep learning approaches have obtained very high performance on many NLP tasks. In this course, students gain a thorough introduction to cutting-edge neural networks for NLP.

Content

What is this course about?

Natural language processing (NLP) or computational linguistics is one of the most important technologies of the information age. Applications of NLP are everywhere because people communicate almost everything in language: web search, advertising, emails, customer service, language translation, virtual agents, medical reports, politics, etc. In the 2010s, deep learning (or neural network) approaches obtained very high performance across many different NLP tasks, using single end-to-end neural models that did not require traditional, task-specific feature engineering. In the 2020s amazing further progress was made through the scaling of Large Language Models, such as ChatGPT. In this course, students will gain a thorough introduction to both the basics of Deep Learning for NLP and the latest cutting-edge research on Large Language Models (LLMs). Through lectures, assignments and a final project, students will learn the necessary skills to design, implement, and understand their own neural network models, using the Pytorch framework.

“Take it. CS221 taught me algorithms. CS229 taught me math. CS224N taught me how to write machine learning models.” – A CS224N student on Carta

Previous offerings

Below you can find archived websites and student project reports from previous years. Disclaimer: assignments change from year to year; please do not do assignments from previous years!

Prerequisites

Proficiency in Python
All class assignments will be in Python (using NumPy and PyTorch). If you need to remind yourself of Python, or you're not very familiar with NumPy, you can come to the Python review session in week 1 (listed in the schedule). If you have a lot of programming experience but in a different language (e.g. C/C++/Matlab/Java/Javascript), you will probably be fine.
College Calculus, Linear Algebra (e.g. MATH 51, CME 100)
You should be comfortable taking (multivariable) derivatives and understanding matrix/vector notation and operations.
Basic Probability and Statistics (e.g. CS 109 or equivalent)
You should know the basics of probabilities, gaussian distributions, mean, standard deviation, etc.
Foundations of Machine Learning (e.g. CS221, CS229, CS230, or CS124)
We will be formulating cost functions, taking derivatives and performing optimization with gradient descent. If you already have basic machine learning and/or deep learning knowledge, the course will be easier; however it is possible to take CS224N without it. There are many introductions to ML, in webpage, book, and video form. One approachable introduction is Hal Daumé’s in-progress A Course in Machine Learning. Reading the first 5 chapters of that book would be good background. Knowing the first 7 chapters would be even better!

Reference Texts

The following texts are useful, but none are required. All of them can be read free online.

Dan Jurafsky and James H. Martin. Speech and Language Processing (2024 pre-release)
Jacob Eisenstein. Natural Language Processing
Yoav Goldberg. A Primer on Neural Network Models for Natural Language Processing
Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Deep Learning
Delip Rao and Brian McMahan. Natural Language Processing with PyTorch (requires Stanford login).
Lewis Tunstall, Leandro von Werra, and Thomas Wolf. Natural Language Processing with Transformers

If you have no background in neural networks but would like to take the course anyway, you might well find one of these books helpful to give you more background:

Michael A. Nielsen. Neural Networks and Deep Learning
Eugene Charniak. Introduction to Deep Learning

Coursework

Disclaimer: Coursework is tentative and subject to change!

Assignments (48%)

There are four weekly assignments, which will improve both your theoretical understanding and your practical skills. All assignments contain both written questions and programming parts. In office hours, TAs may look at students’ code for assignments 1 and 2, but not for assignments 3 and 4.

Credit:
- Assignment 1 (6%): Introduction to word vectors
- Assignment 2 (14%): Neural network foundations, calculating tensor derivatives, dependency parsing
- Assignment 3 (14%): Neural Machine Translation with sequence-to-sequence, attention, and subwords
- Assignment 4 (14%): Self-supervised learning and fine-tuning with Transformers
Deadlines: All assignments are due on either a Tuesday or a Thursday before class (i.e. before 4:30pm). All deadlines are listed in the schedule.
Submission: Assignments are submitted via Gradescope. You will be able to access the course Gradescope page on Canvas. If you need to sign up for a Gradescope account, please use your @stanford.edu email address. Further instructions are given in each assignment handout.Do not email us your assignments.
Late start: If the result gives you a higher grade, we will not use your assignment 1 score, and we will give you an assignment grade based on counting each of assignments 2–4 at 16%.
Collaboration: Study groups are allowed, but students must understand and complete their own assignments, and hand in one assignment per student. If you worked in a group, please put the names of the members of your study group at the top of your assignment. Please ask if you have any questions about the collaboration policy.
Honor Code: We expect students to not look at solutions or implementations online. Like all other classes at Stanford, we take the student Honor Code seriously. We sometimes use automated methods to detect overly similar assignment solutions.

Final Project (49%)

The Final Project offers you the chance to apply your newly acquired skills towards an in-depth application. Students have two options: the Default Final Project (in which students tackle a predefined task, namely implementing a minimalist version of GPT-2) or a Custom Final Project (in which students choose their own project involving human language and deep learning). Examples of both can be seen on last year's website. Note: TAs may not look at students' code for either the default or custom final projects.

Important information

Credit: For both default and custom projects, credit for the final project is broken down as follows:
- Project proposal (8%)
- Project milestone (6%)
- Project poster (3%)
- Project report (32%)
Deadlines: The project proposal, milestone and report are all due at 4:30pm. All deadlines are listed in the schedule.
Default Final Project: In this project, students implement parts of the GPT-2 architecture and use it to tackle 3 downstream tasks. Similar to previous years, the code is in PyTorch.
Project advice [lecture slides] [custom project tips]: The Practical Tips for Final Projects lecture provides guidance for choosing and planning your project. To get project advice from staff members, first look at each staff member's areas of expertise on the office hours page. This should help you find a staff member who is knowledgable about your project area.
Ethics-related questions: For guidance on projects dealing with ethical questions, or ethical questions that arise during your project, please contact Wanheng Hu (wanhenghu@stanford.edu) or Veronica Rivera (varivera@stanford.edu).

Practicalities

Team size: Students may do final projects solo, or in teams of up to 3 people. We strongly recommend you do the final project in a team. Larger teams are expected to do correspondingly larger projects, and you should only form a 3-person team if you are planning to do an ambitious project where every team member will have a significant contribution.
Contribution: In the final report we ask for a statement of what each team member contributed to the project. Team members will typically get the same grade, but we may differentiate in extreme cases of unequal contribution. You can contact us in confidence in the event of unequal contribution.
External collaborators: You can work on a project that has external (non CS224N student) collaborators, but you must make it clear in your final report which parts of the project were your work.
Sharing projects: You can share a single project between CS224N and another class, but we expect the project to be accordingly bigger, and you must declare that you are sharing the project in your project proposal.
Mentors: Every custom project team has a mentor, who gives feedback and advice during the project. Default project teams do not have mentors. A project may have an external (i.e., not course staff) mentor; otherwise, we will assign a CS224N staff mentor to custom project teams after project proposals.
Computing resources: All teams will receive credits to use Google Cloud Platform, thanks to a kind donation by Google!
Using external resources: The following guidelines apply to all projects (though the default project has some more specific rules, details provided in the Honor Code section of the handout):
- You can use any deep learning framework you like (PyTorch, TensorFlow, etc.)
- More generally, you may use any existing code, libraries, etc. and consult any papers, books, online references, etc. for your project. However, you must cite your sources in your writeup and clearly indicate which parts of the project are your contribution and which parts were implemented by others.
- Under no circumstances may you look at another CS224N group's code, or incorporate their code into your project.

Participation (3%)

We appreciate everyone being actively involved in the class! There are several ways of earning participation credit, which is capped at 3%:

Attending guest speakers' lectures:
- In the second half of the class, we have five invited speakers. Our guest speakers make a significant effort to come lecture for us, so (both to show our appreciation and to continue attracting interesting speakers) we do not want them lecturing to a largely empty room. As such, we encourage students to attend these virtual lectures live, and participate in Q&A.
- All students get 0.3% per speaker (1.5% total) for either attending the guest lecture in person, or by writing a reaction paragraph if you watched the talk remotely; details will be provided. Students do not need to attend lecture live to write these reaction paragraphs; they may watch asynchronously.
Completing feedback surveys: We will send out two feedback surveys (mid-quarter and end-of-quarter) to help us understand how the course is going, and how we can improve. Each of the two surveys are worth 0.5%.
Ed participation: The top ~20 contributors to Ed will get 3%; others will get credit in proportion to the participation of the ~20th person.
Karma point: Any other act that improves the class, like helping out another student in office hours or writing a useful guide for students on some topic, which a CS224N TA or instructor notices and deems worthy: 1%

Late Days

Each student has 6 late days to use. A late day extends the deadline 24 hours. You can use up to 3 late days per assignment (including all four assignments, project proposal, project milestone and project final report).
Final project teams can share late days between members. For example, a group of three people must have at least six late days between them to extend the deadline by two days. If any late days are being shared, this must be clearly marked at the beginning of the report, and we will release a form on Ed that teams should fill out..
Once you have used all 6 late days, the penalty is 1% off the final course grade for each additional late day.

Regrade Requests

If you feel you deserved a better grade on an assignment, you may submit a regrade request on Gradescope within 3 days after the grades are released. Your request should briefly summarize why you feel the original grade was unfair. Your TA will reevaluate your assignment as soon as possible, and then issue a decision. If you are still not happy, you can ask for your assignment to be regraded by an instructor.

Credit/No credit enrollment

If you take the class credit/no credit then you are graded in the same way as those registered for a letter grade. The only difference is that, providing you reach a C- standard in your work, it will simply be graded as CR.

All students welcome

We are committed to doing what we can to work for equity and to create an inclusive learning environment that actively values the diversity of backgrounds, identities, and experiences of everyone in CS224N. We also know that we will sometimes make missteps. If you notice some way that we could do better, we hope that you will let someone in the course staff know about it.

Well-Being and Mental Health

If you are experiencing personal, academic, or relationship problems and would like to talk to someone with training and experience, reach out to the Counseling and Psychological Services (CAPS) on campus. CAPS is the university’s counseling center dedicated to student mental health and wellbeing. Phone assessment appointments can be made at CAPS by calling 650-723-3785, or by accessing the VadenPatient portal through the Vaden website.

Auditing the course

In general we are happy to have auditors if they are a member of the Stanford community (registered student, official visitor, staff, or faculty). If you are interested, email us at cs224n-win2425-staff@lists.stanford.edu. If you want to actually master the material of the class, we very strongly recommend that auditors do all the assignments. However, due to high enrollment, we cannot grade the work of any students who are not officially enrolled in the class.

Students with Documented Disabilities

We assume that all of us learn in different ways, and that the organization of the course must accommodate each student differently. We are committed to ensuring the full participation of all enrolled students in this class. If you need an academic accommodation based on a disability, you should initiate the request with the Office of Accessible Education (OAE). The OAE will evaluate the request, recommend accommodations, and prepare a letter for faculty. Students should contact the OAE as soon as possible and at any rate in advance of assignment deadlines, since timely notice is needed to coordinate accommodations. Students should also send your accommodation letter to either the staff mailing list (cs224n-win2425-staff@lists.stanford.edu) or make a private post on Ed, as soon as possible.

OAE accommodations for group projects: OAE accommodations will not be extended to collaborative assignments.

AI Tools Policy

Students are required to independently submit their solutions for CS224N homework assignments. Collaboration with generative AI tools such as Co-Pilot and ChatGPT is allowed, treating them as collaborators in the problem-solving process. However, the direct solicitation of answers or copying solutions, whether from peers or external sources, is strictly prohibited.

Employing AI tools to substantially complete assignments or exams will be considered a violation of the Honor Code. For additional details, please refer to the Generative AI Policy Guidance here.

Sexual violence

Academic accommodations are available for students who have experienced or are recovering from sexual violence. If you would like to talk to a confidential resource, you can schedule a meeting with the Confidential Support Team or call their 24/7 hotline at: 650-725-9955. Counseling and Psychological Services also offers confidential counseling services. Non-confidential resources include the Title IX Office, for investigation and accommodations, and the SARA Office, for healing programs. Students can also speak directly with the teaching staff to arrange accommodations. Note that university employees – including professors and TAs – are required to report what they know about incidents of sexual or relationship violence, stalking and sexual harassment to the Title IX Office. Students can learn more at [https://vaden.stanford.edu/sexual-assault](are recovering from).

Schedule

Updated lecture slides will be posted here shortly before each lecture. Other links contain last year's slides, which are mostly similar.

Lecture notes will be uploaded a few days after most lectures. The notes (which cover approximately the first half of the course content) give supplementary detail beyond the lectures.

Disclaimer: Schedule is tentative and subject to change!
Disclaimer: Assignments change; please do not do old assignments. We will give no points for doing last year's assignments.

Date	Description	Course Materials	Events	Deadlines
Week 1Tue Jan 7	Word Vectors [slides] [notes]	Suggested Readings: Efficient Estimation of Word Representations in Vector Space (original word2vec paper) Distributed Representations of Words and Phrases and their Compositionality (negative sampling paper)	Assignment 1 out [code]
Thu Jan 9	Word Vectors and Language Models [slides] [notes] [code]	Suggested Readings: GloVe: Global Vectors for Word Representation (original GloVe paper) Improving Distributional Similarity with Lessons Learned from Word Embeddings Evaluation methods for unsupervised word embeddings Additional Readings: A Latent Variable Model Approach to PMI-based Word Embeddings Linear Algebraic Structure of Word Senses, with Applications to Polysemy On the Dimensionality of Word Embedding
Fri Jan 10	Python Review Session [[slides](slides%5Fw25/2024 CS224N Python Review Session Slides.pptx.pdf)] [colab]	Time 1:30pm-2:20pm Location Gates B01
Week 2Tue Jan 14	Backpropagation and Neural Network Basics [slides] [notes]	Suggested Readings: matrix calculus notes Review of differential calculus CS231n notes on network architectures CS231n notes on backprop Derivatives, Backpropagation, and Vectorization Learning Representations by Backpropagating Errors (seminal Rumelhart et al. backpropagation paper) Additional Readings: Yes you should understand backprop Natural Language Processing (Almost) from Scratch	Assignment 2 out [code] [handout] [latex template]	Assignment 1 due
Thu Jan 16	Dependency Parsing [slides] [notes]	Suggested Readings: Incrementality in Deterministic Dependency Parsing A Fast and Accurate Dependency Parser using Neural Networks Dependency Parsing Globally Normalized Transition-Based Neural Networks Universal Stanford Dependencies: A cross-linguistic typology Universal Dependencies website Jurafsky & Martin Chapter 19
Fri Jan 17	PyTorch Tutorial Session[colab]	Time 1:30pm-2:20pm Location Gates B01
Week 3Tue Jan 21	Basic Sequence Models to RNNs [slides] [notes (lectures 5 and 6)]	Suggested Readings: N-gram Language Models (textbook chapter) The Unreasonable Effectiveness of Recurrent Neural Networks (blog post overview) Sequence Modeling: Recurrent and Recursive Neural Nets (Sections 10.1 and 10.2) On Chomsky and the Two Cultures of Statistical Learning
Thu Jan 23	Advanced Variants of RNNs, Attention [slides] [notes (lectures 5 and 6)]	Suggested Readings: Learning long-term dependencies with gradient descent is difficult (one of the original vanishing gradient papers) On the difficulty of training Recurrent Neural Networks (proof of vanishing gradient problem) Vanishing Gradients Jupyter Notebook (demo for feedforward networks) Attention Is All You Need	Assignment 3 out [code] [handout] [latex template]	Assignment 2 due
Week 4Tue Jan 28	Final Projects: Custom and Default; Practical Tips [slides]	Suggested Readings: Practical Methodology (Deep Learning book chapter)
Thu Jan 30	Transformers [slides] [Custom project tips] [notes]	Suggested Readings: Attention Is All You Need The Illustrated Transformer Transformer (Google AI blog post) Layer Normalization Image Transformer Music Transformer: Generating music with long-term structure Jurafsky and Martin Chapter 9 (The Transformer)	Project Proposal out [handout] Default Final Project out [handout]
Week 5Tue Feb 4	Pretraining [slides]	Suggested Readings: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding Contextual Word Representations: A Contextual Introduction The Illustrated BERT, ELMo, and co. Jurafsky and Martin Chapter 11 (Masked Language Models)	Assignment 4 out [code] [handout] [overleaf] [colab run script]	Assignment 3 due
Thu Feb 6	Post-training (RLHF, SFT, DPO) [slides]	Suggested Readings: Aligning language models to follow instructions Scaling Instruction-Finetuned Language Models AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedback How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources Direct Preference Optimization: Your Language Model is Secretly a Reward Model
Fri Feb 7	Hugging Face Transformers Tutorial Session [colab]	Time 1:30pm-2:20pm Location Gates B01
Week 6Tue Feb 11	Efficient Adaptation (Prompting + PEFT) [slides]	Suggested Readings: Language Models are Few-Shot Learners Chain-of-Thought Prompting Elicits Reasoning in Large Language Models The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks LoRA: Low-Rank Adaptation of Large Language Models Parameter-Efficient Transfer Learning for NLP	Project Proposal due
Thu Feb 13	Benchmarking and Evaluation [slides]	Suggested Readings: Challenges and Opportunities in NLP Benchmarking Measuring Massive Multitask Language Understanding Holistic Evaluation of Language Models AlpacaEval	Project Milestone out [handout]	Assignment 4 due
Week 7Tue Feb 18	Question Answering and Knowledge [slides]	Suggested readings: SQuAD: 100,000+ Questions for Machine Comprehension of Text Dense Passage Retrieval for Open-Domain Question Answering Bidirectional Attention Flow for Machine Comprehension Reading Wikipedia to Answer Open-Domain Questions REALM: Retrieval-Augmented Language Model Pre-Training Lost in the Middle: How Language Models Use Long Contexts	Final Project Proposals Returned
Thu Feb 20	Guest Lecture (Alexander Rush)	Suggested readings:
Week 8Tue Feb 25	Guest Lecture: A Retrieval-based LM at Scale (Sewon Min)	Suggested readings: Dense Passage Retrieval for Open-Domain Question Answering In-Context Retrieval-Augmented Language Models Scaling Retrieval-Based Language Models with a Trillion-Token Datastore SILO Language Models: Isolating Legal Risk In a Nonparametric Datastore	Final Project Milestone due
Thu Feb 27	Guest Lecture: The Second Half: Model Evaluation and Benchmarking (Shunyu Yao)	Suggested readings: WebShop: Towards Scalable Rea-World Web Interaction with Grounded Language Agents Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena SWE-bench: Can Language Models Resolve Real-World GitHub Issues? Tau-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains	Final Project Report Instructions out [Instructions]
Fri Feb 28			Course Withdrawal Deadline
Week 9Tue Mar 4	Guest Lecture: Model Analysis and Interpretability (Jing Huang) [slides]	Suggested readings: Adversarial Examples for Evaluating Reading Comprehension Systems BERT Rediscovers the Classical NLP Pipeline Axiomatic Attribution for Deep Networks Investigating Gender Bias in Language Models Using Causal Mediation Analysis Faithful, Interpretable Model Explanations via Causal Abstraction	Final Project Milestones Returned
Thu Mar 6	Guest Lecture: Reasoning (Noam Brown)	Suggested readings: Learning to Reason with LLMs OpenAI o1 System Card DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
Week 10Tue Mar 11	Open Questions in NLP 2025
Thu Mar 13	Final Project Emergency Assistance (No Lecture)	Final project due
Finals Week	Final Project Poster Session	Time: Mar 18th, Time 12:15pm-3:15pm Location AOERC On-campus students must attend in person!	[Printing guide]