machine learning – Page 31 – Andrew Fairless, Ph.D.

What I Read: Lessons on ML Platforms

By Andrew Fairless on December 20, 2021November 16, 2021

https://towardsdatascience.com/lessons-on-ml-platforms-from-netflix-doordash-spotify-and-more-f455400115c7 Lessons on ML Platforms — from Netflix, DoorDash, Spotify, and moreErnest ChanMay 12 “Many companies who do ML well have invested in ML platforms to enable data scientists toContinue readingWhat I Read: Lessons on ML Platforms

What I Read: Deep Learning Optimization Theory

By Andrew Fairless on December 15, 2021November 16, 2021

https://towardsdatascience.com/deep-learning-optimization-theory-introduction-148b3504b20f?gi=ff0bd10cc9fe Deep Learning Optimization Theory — IntroductionUnderstanding the theory of optimization in deep learning is crucial to enable progress. This post introduces the experimental and theoretical approaches to studying it.OmriContinue readingWhat I Read: Deep Learning Optimization Theory

What I Read: Dense Vectors

By Andrew Fairless on December 14, 2021November 16, 2021

https://www.pinecone.io/learn/dense-vector-embeddings-nlp/ Dense Vectors: Capturing Meaning with CodeJames BriggsData Scientist “But since the days of word2vec, developments in representing language have advanced at ludicrous speeds. This article will explore why weContinue readingWhat I Read: Dense Vectors

What I Read: Non-Technical Guide to Interpreting SHAP

By Andrew Fairless on December 13, 2021November 16, 2021

https://www.aidancooper.co.uk/a-non-technical-guide-to-interpreting-shap-analyses/ Explaining Machine Learning Models: A Non-Technical Guide to Interpreting SHAP AnalysesAidan CooperNov 1, 2021With interpretability becoming an increasingly important requirement for machine learning projects, there’s a growing need forContinue readingWhat I Read: Non-Technical Guide to Interpreting SHAP

What I Read: True Stories of Algorithmic Improvement

By Andrew Fairless on December 8, 2021November 16, 2021

https://www.greaterwrong.com/posts/RoyG2GwYAwoka47Ht/true-stories-of-algorithmic-improvement True Stories of Algorithmic Improvementjohnswentworth29 Oct 2021 20:57 UTC “Algorithms beating compute is the sort of thing I expect by default, on a gut level. The point of thisContinue readingWhat I Read: True Stories of Algorithmic Improvement

What I Read: Her Machine Learning Tools Pull Insights From Cell Images

By Andrew Fairless on December 6, 2021November 16, 2021

https://www.quantamagazine.org/anne-carpenters-ai-tools-pull-insights-from-cell-images-20211102/ Her Machine Learning Tools Pull Insights From Cell ImagesEsther LandhuisContributing WriterNovember 2, 2021The computational biologist Anne Carpenter creates software that brings the power of machine learning to researchers seekingContinue readingWhat I Read: Her Machine Learning Tools Pull Insights From Cell Images

What I Read: Autonomous Building of Composable Models

By Andrew Fairless on December 1, 2021November 16, 2021

https://thegradient.pub/strong-ai-requires-autonomous-building-of-composable-models/ Strong AI Requires Autonomous Building of Composable Models30.Oct.2021Jonathan MuganDr. Jonathan Mugan is a principal scientist at DeUmbra and is the author of The Curiosity Cycle. “…AI must be ableContinue readingWhat I Read: Autonomous Building of Composable Models

What I Read: machine learning with differential privacy

By Andrew Fairless on November 29, 2021November 16, 2021

https://differentialprivacy.org/how-to-deploy-ml-with-dp/ How to deploy machine learning with differential privacy?Posted by Nicolas Papernot and Abhradeep Thakurta on October 25, 2021. “In many applications of machine learning, such as machine learning forContinue readingWhat I Read: machine learning with differential privacy

What I Read: Machine learning is just statistics + quantifier reversal

By Andrew Fairless on November 18, 2021November 16, 2021

https://jeremybernste.in/writing/ml-is-just-statistics Machine learning is just statistics + quantifier reversaljeremybernsteLos Angeles, 21 Oct 2021 “But in machine learning there’s a switcheroo—we select the sample of data first, and then we useContinue readingWhat I Read: Machine learning is just statistics + quantifier reversal

What I Read: First-Principles Theory of Neural Network Generalization

By Andrew Fairless on November 17, 2021November 16, 2021

https://natluk.net/a-first-principles-theory-of-neuralnetwork-generalization-the-berkeley-artificial-intelligence-research-blog/ A First-Principles Theory of Neural Network Generalization – The Berkeley Artificial Intelligence Research BlogNatLuk Community25 October 2021 “Perhaps the greatest of these mysteries has been the question of generalization:Continue readingWhat I Read: First-Principles Theory of Neural Network Generalization

Tag: machine learning