Distributed and Decentralized Learning - Ce Zhang | Stanford MLSys #68

Pathways Language Model and Model Scaling - Aakanksha Chowdhery | Stanford MLSys #69

Text2SQL: The Dream versus Reality - Laurel Orr | Stanford MLSys #89

Serving 100s of LLMs on 1 GPU with LoRAX - Travis Addair | Stanford MLSys #84

Ray, a Unified Distributed Framework for the Modern AI Stack | Ion Stoica

Building RedPajama | Ce Zhang

A friendly introduction to distributed training (ML Tech Talks)

Foundation Models on Consumer Devices - Tianqi Chen | Stanford MLSys #85

Training LLMs at Scale - Deepak Narayanan | Stanford MLSys #83

11. Introduction to Machine Learning

Hardware-aware Algorithms for Sequence Modeling - Tri Dao | Stanford MLSys #87

The Next 100x - Gavin Uberti | Stanford MLSys #92

Think Fast, Talk Smart: Communication Techniques

PipeDream: Generalized Pipeline Parallelism for DNN Training

How to Start Coding | Programming for Beginners | Learn Coding | Intellipaat

Scaling Up “Vibe Checks” for LLMs - Shreya Shankar | Stanford MLSys #97

Monarch Mixer: Making Foundation Models More Efficient - Dan Fu | Stanford MLSys #86

Gauri Joshi: Tackling Computational and Data Heterogeneity in Federated Learning

How Fine-tuning Open Source LLMs Solves GenAI Productionization - Piero Molino | Stanford MLSys #94

Machine Learning Tutorial | Machine Learning Basics | Machine Learning Algorithms | Simplilearn

Multimodal Reasoning: PaLM-E & Gemini - Aakanksha Chowdhery | Stanford MLSys #90