Subject
14 entries
Stanford
Bookmarks
DSPy: Compiling Declarative Language Model Calls (SBTB23 talk)
Omar Khattab's ScalaBytesConf 2023 talk introducing DSPy — the core presentation that launched the framework into wider attention. Covers the compile-don't-prompt philosophy and demonstrates self-improving LLM pipelines.
Stanford CS 329S — Machine Learning Systems Design
Stanford CS 329S Machine Learning Systems Design — Chip Huyen's course on building production ML systems. Covers the full lifecycle from problem framing through data, training, deployment, and monitoring with real-world case studies.
CS231n: Convolutional Neural Networks for Visual Recognition
Stanford CS231n: Convolutional Neural Networks for Visual Recognition — Andrej Karpathy's course that became the de facto entry point into deep learning for computer vision. The lecture notes remain among the best written explanations of CNNs, backprop, and training practice.
Stanford CoreNLP: Free Text Analysis Pipeline
Stanford's CoreNLP — the Java-based NLP pipeline from Stanford's NLP Group offering tokenization, POS tagging, NER, parsing, coreference resolution, and sentiment analysis in one tool. The dominant academic NLP pipeline before deep learning NLP models took over.
UFLDL Tutorial — Stanford
Andrew Ng's Stanford UFLDL Tutorial — the primary self-study resource for deep learning before MOOCs existed. Teaches sparse autoencoders, PCA, CNNs, and deep belief networks with mandatory from-scratch implementation exercises.
Recursive Deep Models for Semantic Compositionality
Stanford's Recursive Neural Tensor Network paper and Sentiment Treebank dataset — Richard Socher's EMNLP 2013 work that used tree-structured recursive neural networks to predict fine-grained sentiment at every node of a parse tree. A landmark paper that pushed NLP models toward compositionality.
Stanford CS229: Machine Learning Course Materials
Stanford CS229 Machine Learning course materials page — Andrew Ng's graduate ML course whose lecture notes became canonical self-study references. The notes on supervised learning, SVMs, neural networks, and probabilistic models were some of the clearest technical writing in the field.
Mining of Massive Datasets (Stanford)
The Stanford textbook by Rajaraman and Ullman on algorithms for mining massive datasets — locality-sensitive hashing, PageRank, collaborative filtering, stream algorithms, and more. Freely available online and a standard reference for large-scale data algorithms.
Aho/Ullman Foundations of Computer Science
The classic undergraduate CS foundations textbook by Alfred Aho and Jeffrey Ullman, freely available from Stanford. Covers data structures, algorithms, automata, and the mathematical foundations underpinning computer science as a discipline.
Biological Computer: Stanford's Genetic Transistors Turn Cells into Computers
Stanford researchers created genetic transistors that turn living cells into biological computers — RNA-based logic gates that implement Boolean operations inside E. coli. A 2013 milestone in synthetic biology demonstrating programmable cellular computation.
SNAP: Stanford Network Analysis Project Manual
The manual for SNAP (Stanford Network Analysis Project) v0.15 — Jure Leskovec's C++ library for large-scale graph analysis and network mining. One of the earliest high-performance graph analysis toolkits, predating Spark GraphX and Neo4j's graph algorithms by years.
Is Stanford Too Close to Silicon Valley?
Ken Auletta's New Yorker investigation into whether Stanford University has become too entangled with Silicon Valley — raising questions about whether the pursuit of startup equity compromises academic independence and the university's long-term mission.
Peter Thiel's CS183: Startup — Class 5 Notes
Blake Masters' notes from Peter Thiel's CS183 Stanford startup course, Class 5 — covering business strategy, competitive advantages, and how to think about building defensible companies. These notes later became the book Zero to One.
How Big Data Is Going to Change Entrepreneurship
Summary of the 2012 Stanford entrepreneurship conference on Big Data — panelists argued data was growing faster than Moore's Law, creating the next oil economy, with advertising and insurance as the most immediately impacted sectors. An early articulation of what became the data economy thesis.