Link Dump

This post is a dumping ground for links I found interesting or useful. Sometimes, I only glanced at them very quickly to add to my read-later.

For resources I genuinely recommend, see this list.

Articles/blogposts:

• “Which GPU(s) to Get for Deep Learning: My Experience and Advice for Using GPUs in Deep Learning” by Tim Dettmers: https://timdettmers.com/2023/01/30/which-gpu-for-deep-learning/
• “Fuck You, Show Me The Prompt.” by Hamel Husain: https://hamel.dev/blog/posts/prompt/
• “Common statistical tests are linear models (or: how to teach stats)” by Jonas Kristoffer Lindeløv: https://lindeloev.github.io/tests-as-linear/
• “Should You Run a Survey?” by Maddie Brown: https://www.nngroup.com/articles/should-you-run-a-survey/
• “Understanding UMAP” by Andy Coenen and Adam Pearce: https://pair-code.github.io/understanding-umap/
• “Deep Playground”: http://playground.tensorflow.org/
• “AI safety is not a model property” by Arvind Narayanan and Sayash Kapoor: https://www.aisnakeoil.com/p/ai-safety-is-not-a-model-property
• “Models All The Way Down” by Christo Buschek and Jer Thorp: https://knowingmachines.org/models-all-the-way (investigation of LAION-5B)
• “Mental Models: The Best Way to Make Intelligent Decisions (~100 Models Explained)”: https://fs.blog/mental-models/

Papers:

• “Pretrained Transformers for Text Ranking: BERT and Beyond” by Jimmy Lin, Rodrigo Nogueira, Andrew Yates: https://arxiv.org/abs/2010.06467
• “Estimating logit models with small samples” by Carlisle Rainey and Kelly McCaskey: https://www.cambridge.org/core/journals/political-science-research-and-methods/article/estimating-logit-models-with-small-samples/712F6039372475B650B123E9A1FE60E6
• “What are the most important statistical ideas of the past 50 years?” by Andrew Gelman and Aki Vehtari: https://arxiv.org/abs/2012.00174
• “Combining Doubly Robust Methods and Machine Learning for Estimating Average Treatment Effects for Observational Real-world Data” by Xiaoqing Tan, Shu Yang, Wenyu Ye, Douglas Faries, Ilya Lipkovich, Zbigniew Kadziola: https://arxiv.org/abs/2204.10969

Books:

• “Deep Learning: Foundations and Concepts” by Christopher Bishop and Hugh Bishop: https://www.bishopbook.com/
• “Engineering a Safer World: Systems Thinking Applied to Safety” by Nancy G. Leveson: https://direct.mit.edu/books/oa-monograph/2908/Engineering-a-Safer-WorldSystems-Thinking-Applied
• “Fairness and Machine Learning: Limitations and Opportunities” by Solon Barocas, Moritz Hardt, and Arvind Narayanan: https://fairmlbook.org/
• “Active Statistics” by Andrew Gelman and Aki Vehtari: https://avehtari.github.io/ActiveStatistics/
• “Improving Your Statistical Inferences” by Daniël Lakens: https://lakens.github.io/statistical_inferences/
• “Teaching Accessible Computing” by Alannah Oleson, Amy J. Ko, and Richard Ladner: https://bookish.press/tac