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CS336
CS336
Overview
CS336 · Language Modeling from Scratch — Notes
CS336 · Lecture 1 — Overview & Tokenization
CS336 · Lecture 2 — Resource Accounting
CS336 · Lecture 3 — Architectures & Hyperparameters
CS336 · Lecture 4 — Attention Alternatives & Mixture of Experts
CS336 · Lecture 5 — GPUs & Making Them Go Fast
CS336 · Lecture 6 — Triton Kernels, Benchmarking & Profiling
CS336 · Lecture 7 — Multi-GPU Parallelism
CS336 · Lecture 8 — Parallelism Deep Dive (ZeRO & 4D)
CS336 · Lecture 9 — Scaling Laws (Basics)
CS336 · Lecture 10 — Inference
CS336 · Lecture 11 — Scaling Laws II (Recipes, Optimizers, μP)
CS336 · Lecture 12 — Evaluation
CS336 · Lecture 13 — Data I (Sources, Copyright, Datasets)
CS336 · Lecture 14 — Data II (Pipeline & Post-training)
CS336 · Lecture 15 — Alignment: SFT & RLHF
CS336 · Lecture 16 — RLVR & Reasoning Models
CS336 · Lecture 17 — Multimodality
CS336 · Lecture 18 — Inference Systems (Guest)
ML Sys Design
ML Sys Design
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FB News Feed Ranking — ML System Design Master Notes
Deep Neural Networks for YouTube Recommendations — Master Notes
Guides
Guides
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Python Typing
UV for Python
Weight Initialization in DL
ML Notes
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Notes on Machine Learning — fundamentals, models, training, and applied topics.
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