machine learning – Page 47 – Andrew Fairless, Ph.D.

What I Read: Production with Deep Semi-Supervised Learning

By Andrew Fairless on January 28, 2021January 2, 2021

https://towardsdatascience.com/from-research-to-production-with-deep-semi-supervised-learning-7caaedc39093 From Research to Production with Deep Semi-Supervised LearningVarun NairSep 25 “Semi-supervised learning (SSL), a subfield that combines both supervised and unsupervised learning, has grown in popularity in the deepContinue readingWhat I Read: Production with Deep Semi-Supervised Learning

What I Read: AI Can Help Patients If Doctors Understand It

By Andrew Fairless on January 27, 2021January 1, 2021

https://www.wired.com/story/ai-help-patients-doctors-understand/ Tom SimoniteBusiness10.02.2020 08:00 AMAI Can Help Patients—but Only If Doctors Understand It Algorithms can help diagnose a growing range of health problems, but humans need to be trained toContinue readingWhat I Read: AI Can Help Patients If Doctors Understand It

What I Read: data quality, ML Ops

By Andrew Fairless on January 27, 2021January 1, 2021

https://greatexpectations.io/blog/ml-ops-data-quality/ Why data quality is key to successful ML OpsIn this first post in our 2-part ML Ops series, we are going to look at ML Ops and highlight howContinue readingWhat I Read: data quality, ML Ops

What I Read: AI’s limitations

By Andrew Fairless on January 26, 2021December 31, 2020

https://www.economist.com/technology-quarterly/2020/06/11/an-understanding-of-ais-limitations-is-starting-to-sink-in Technology QuarterlyJun 11th 2020 editionAn understanding of AI’s limitations is starting to sink inAfter years of hype, many people feel AI has failed to deliver, says Tim Cross “SurveyingContinue readingWhat I Read: AI’s limitations

What I Read: Neural Architecture Search

By Andrew Fairless on January 26, 2021December 31, 2020

https://lilianweng.github.io/lil-log/2020/08/06/neural-architecture-search.html Neural Architecture SearchAug 6, 2020 by Lilian Weng “Automatically learning and evolving network topologies is not a new idea (Stanley & Miikkulainen, 2002). In recent years, the pioneering workContinue readingWhat I Read: Neural Architecture Search

What I Read: This AI learns by reading the web

By Andrew Fairless on January 25, 2021December 31, 2020

https://www.technologyreview.com/2020/09/04/1008156/knowledge-graph-ai-reads-web-machine-learning-natural-language-processing/ This know-it-all AI learns by reading the entire web nonstopDiffbot is building the biggest-ever knowledge graph by applying image recognition and natural-language processing to billions of web pages.Will DouglasContinue readingWhat I Read: This AI learns by reading the web

What I Read: Best Practices for Building Machine Learning at Scale

By Andrew Fairless on January 24, 2021December 30, 2020

https://medium.com/dataseries/linkedins-pro-ml-architecture-summarizes-best-practices-for-building-machine-learning-at-scale-77fcb6afc9ec LinkedIn’s Pro-ML Architecture Summarizes Best Practices for Building Machine Learning at ScaleThe reference architecture is powering mission critical machine learning workflows within LinkedIn.Jesus RodriguezSep 17 “Building machine learning solutionsContinue readingWhat I Read: Best Practices for Building Machine Learning at Scale

What I Read: Can Neural Networks Show Imagination?

By Andrew Fairless on January 24, 2021December 30, 2020

https://medium.com/dataseries/can-neural-networks-show-imagination-deepmind-thinks-they-can-b3b874aece67 Can Neural Networks Show Imagination? DeepMind Thinks they CanDeepMind has done some of the relevant work in the area of simulating imagination in deep learning systems.Jesus RodriguezSep 14 “IncorporatingContinue readingWhat I Read: Can Neural Networks Show Imagination?

What I Read: Traffic prediction with Graph Neural Networks

By Andrew Fairless on January 23, 2021December 30, 2020

https://deepmind.com/blog/article/traffic-prediction-with-advanced-graph-neural-networks Traffic prediction with advanced Graph Neural Networks “We discovered that Graph Neural Networks are particularly sensitive to changes in the training curriculum… the most successful approach to this problemContinue readingWhat I Read: Traffic prediction with Graph Neural Networks

What I Read: Continual Learning… and the Bridge to Active and Open World Learning

By Andrew Fairless on January 22, 2021December 30, 2020

https://arxiv.org/abs/2009.01797 A Wholistic View of Continual Learning with Deep Neural Networks: Forgotten Lessons and the Bridge to Active and Open World LearningMartin Mundt, Yong Won Hong, Iuliia Pliushch, Visvanathan RameshContinue readingWhat I Read: Continual Learning… and the Bridge to Active and Open World Learning

Tag: machine learning