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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.

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Community Sentiment

Mixed

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.