Stanford CS336: Language Modeling from Scratch Course Announced
Original: CS336: Language Modeling from Scratch
Why This Matters
Addresses growing demand for deep language model expertise in modern AI development
Stanford University announces CS336, a comprehensive course teaching students to build language models from scratch. The Spring 2026 course covers data collection, transformer architecture, training, and evaluation, requiring advanced Python proficiency and systems optimization skills.
Stanford CS336 provides hands-on experience in developing complete language models, drawing inspiration from operating systems courses. Students implement tokenizers, model architectures, and optimizers, then train transformer models from minimal scaffolding. The curriculum includes four major assignments: building basic transformer components, systems optimization with FlashAttention2 implementation, scaling law studies, and data processing from Common Crawl dumps. Prerequisites include Python proficiency, deep learning experience with PyTorch, calculus, linear algebra, and machine learning fundamentals. Instructors Tatsunori Hashimoto and Percy Liang emphasize this as an implementation-heavy 5-unit course requiring significant time commitment.