Description
Stanford CS336, titled 'Language Modeling from Scratch', is an advanced course offered in Spring 2026 that delves into the intricacies of building and understanding modern language models. This course is designed for students who wish to gain a deep understanding of language models, which are fundamental to contemporary natural language processing (NLP) applications. As the fields of artificial intelligence (AI) and machine learning (ML) continue to evolve, a thorough grasp of language models is becoming increasingly vital for both scientists and engineers.
The course structure is inspired by traditional operating systems courses, guiding students through the entire process of developing their own language models. This includes critical aspects such as data collection and cleaning for pre-training, constructing transformer models, training, and evaluating models before deployment. The curriculum emphasizes practical implementation, requiring students to engage deeply with the material and produce substantial coding work.
Prerequisites for the course include proficiency in Python, experience with deep learning and systems optimization, and a solid foundation in college-level calculus, linear algebra, and basic probability and statistics. Students are expected to be familiar with machine learning concepts and frameworks, particularly PyTorch, as the course involves significant hands-on programming.
Assignments are structured to progressively build students' skills, starting with the implementation of basic components necessary for training a standard transformer language model. Subsequent assignments focus on optimizing model performance, scaling, and applying reinforcement learning techniques for reasoning tasks. The course also provides resources for students to access GPU compute for self-study, ensuring they have the necessary tools to complete their assignments effectively.
With a strong emphasis on collaboration and academic integrity, students are encouraged to work in study groups while ensuring they understand and complete their own assignments. The course promotes the use of AI tools for low-level programming questions but discourages reliance on them for solving assignment problems directly. Overall, CS336 offers a rigorous and comprehensive approach to understanding and developing language models, preparing students for advanced work in AI and NLP.
CS336: Language Modeling from Scratch's Core Features
Hands-on assignments
Focus on transformer models
Emphasis on Python programming
Collaboration encouraged
GPU compute access for assignments
Implementation-heavy curriculum
Reinforcement learning techniques
Deep learning optimization
How to use CS336: Language Modeling from Scratch?
Understand prerequisites: Ensure you meet the course prerequisites in Python, deep learning, and mathematics.
Attend lectures: Participate in the scheduled lectures on Mondays and Wednesdays.
Engage in assignments: Complete the hands-on assignments to build your own language models.
Utilize resources: Access GPU compute resources as needed for your assignments.
CS336: Language Modeling from Scratch's Use Cases
- Language model development
- Deep learning optimization
- Data preprocessing
- Reinforcement learning applications
- Collaborative learning
