Introduction
In the ever-evolving landscape of technology, few fields are as dynamic and impactful as artificial intelligence (AI). Among the various subfields of AI, large language models (LLMs) have garnered significant attention for their transformative applications in natural language processing (NLP). Recognizing the growing importance of this technology, Stanford University has taken a pioneering step by offering its CS336 course in Spring 2025, titled Language Models from Scratch. This course provides a comprehensive guide to creating language models from the ground up, and its materials have been made publicly available to foster a deeper understanding of this cutting-edge technology.
Course Overview
What is CS336?
CS336 at Stanford University is a unique course designed to equip students with the theoretical knowledge and practical skills needed to build language models from scratch. The course covers a broad spectrum of topics, ranging from the mathematical foundations of machine learning to the practical implementation of LLMs.
Why From Scratch?
The from scratch approach is central to the pedagogical philosophy of CS336. By building models from the ground up, students gain an in-depth understanding of the underlying mechanics of LLMs. This method not only demystifies the complex algorithms powering modern NLP applications but also empowers students to innovate and customize models to suit specific needs.
Course Materials and Resources
The course materials, including lecture videos and supplementary resources, are available online. The lecture videos can be accessed via YouTube, and the course homepage serves as a comprehensive repository of all relevant materials and information.
- Lecture Videos: CS336 YouTube Playlist
- Course Homepage: CS336 Course Website
Faculty and Instructors
The success of any academic course heavily relies on the expertise and dedication of its teaching staff. CS336 boasts an impressive lineup of instructors, each bringing a wealth of knowledge and experience in the field of machine learning and NLP.
Tatsunori Hashimoto
Tatsunori Hashimoto, an Assistant Professor in the Computer Science Department at Stanford University, is one of the lead instructors for CS336. Before joining Stanford, Hashimoto was a postdoctoral researcher working with John C. Duchi and Percy Liang. His research focuses on the trade-offs between the average and worst-case performances of machine learning models.
Hashimoto’s academic journey includes a Ph.D. from MIT, where he was advised by Tommi Jaakkola and David Gifford. His undergraduate studies were completed at Harvard University, where he majored in statistics and mathematics under the guidance of Edoardo Airoldi. With over 30,000 citations to his research, Hashimoto is a recognized authority in the field of machine learning.
Percy Liang
Percy Liang, an Associate Professor in the Computer Science Department at Stanford University, is another key figure behind CS336. Liang also serves as the director of the Center for Research on Foundation Models (CRFM). His research interests span machine learning, natural language processing, and probabilistic models.
Liang’s contributions to the field are extensive, and his work has been widely cited and acclaimed. As the director of CRFM, he plays a crucial role in advancing the understanding and development of foundation models, which form the backbone of modern LLMs.
Course Structure and Content
Week-by-Week Breakdown
CS336 is structured to provide a gradual and comprehensive understanding of language models. The course is divided into weekly modules, each focusing on a specific aspect of LLM development.
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Week 1-2: Introduction to Language Models
- Overview of NLP and the role of language models
- Historical context and evolution of LLMs
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Week 3-4: Mathematical Foundations
- Probability and statistics essentials
- Linear algebra and calculus for machine learning
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Week 5-6: Core Algorithms
- Understanding transformers and attention mechanisms
- Implementing basic models
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**Week 7-8: Advanced Topics
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