CS336: Language Modeling from Scratch
cs336.stanford.edu
June 1, 2026
7 min read
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69/100
Summary
Stanford CS336 focuses on language modeling from scratch, with lectures held on Mondays and Wednesdays from 3:00-4:20pm in Skilling Auditorium. Office hours are available for course staff at various times and locations throughout the week.
Key Takeaways
- The course CS336 at Stanford focuses on developing language models from scratch, covering data collection, model construction, training, and evaluation.
- Prerequisites for the course include proficiency in Python, experience with deep learning and systems optimization, and a background in college calculus, linear algebra, and basic probability and statistics.
- The coursework includes five assignments that involve implementing components of a Transformer model, optimizing model performance, and applying reinforcement learning for reasoning tasks.
- Students are expected to allocate significant time for implementation-heavy assignments, which will require a deep understanding of machine learning concepts.
Community Sentiment
Positives
- The course allows for scaling down to local compute, making it accessible for learners without high-end GPUs, which democratizes AI education.
- Students have successfully trained reasonably good models on lower-end GPUs, indicating that effective learning and experimentation can occur without expensive resources.
- The updates to assignments, particularly in alignment and distributed tasks, reflect a commitment to keeping the curriculum relevant and challenging.
Concerns
- Concerns were raised about the necessity of high-cost GPU resources for beginners, suggesting a potential barrier to entry for those new to AI.
- Some users expressed confusion about why the educational institution does not provide required resources, highlighting a gap in support for students.
