machine learning – Page 50 – Andrew Fairless, Ph.D.

What I Read: Deep Double Descent: Where Bigger Models and More Data Hurt

By Andrew Fairless on February 6, 2021January 11, 2021

https://arxiv.org/abs/1912.02292 Deep Double Descent: Where Bigger Models and More Data HurtPreetum Nakkiran, Gal Kaplun, Yamini Bansal, Tristan Yang, Boaz Barak, Ilya Sutskever “We show that a variety of modern deepContinue readingWhat I Read: Deep Double Descent: Where Bigger Models and More Data Hurt

What I Read: AI for good, think form extraction

By Andrew Fairless on February 5, 2021January 10, 2021

http://jonathanstray.com/to-apply-ai-for-good-think-form-extraction To apply AI for good, think form extractionPosted on October 24, 2020 Author Jonathan Stray “Folks who want to use AI/ML for good generally think of things like buildingContinue readingWhat I Read: AI for good, think form extraction

What I Read: Explainable AI, 2-Stage Approach

By Andrew Fairless on February 5, 2021January 10, 2021

https://www.kdnuggets.com/2020/10/explaining-explainable-ai.html Explaining the Explainable AI: A 2-Stage ApproachBy Amit Dhurandhar, Research Scientist at IBM. “XAI is primarily concerned with understanding or interpreting the decisions made by these opaque or black-boxContinue readingWhat I Read: Explainable AI, 2-Stage Approach

What I Read: “Less than one”-shot learning

By Andrew Fairless on February 4, 2021January 10, 2021

https://www.technologyreview.com/2020/10/16/1010566/ai-machine-learning-with-tiny-data/ Artificial intelligence/Machine learningA radical new technique lets AI learn with practically no data“Less than one”-shot learning can teach a model to identify more objects than the number of examplesContinue readingWhat I Read: “Less than one”-shot learning

What I Read: Architectures for Modern Data Infrastructure

By Andrew Fairless on February 4, 2021January 10, 2021

https://a16z.com/2020/10/15/the-emerging-architectures-for-modern-data-infrastructure/ Emerging Architectures for Modern Data Infrastructureby Matt Bornstein, Martin Casado, and Jennifer Li “We’re now starting to see the rise of massive, complex systems built around data – whereContinue readingWhat I Read: Architectures for Modern Data Infrastructure

What I Read: What Color Is This? Part 2

By Andrew Fairless on February 3, 2021January 8, 2021

https://multithreaded.stitchfix.com/blog/2020/10/13/what-color-is-this-part-2/ What Color Is This? Part 2Blog post by Dan TasseDan TasseOctober 13, 2020 – San Francisco, CAWhat Color Is This, Part 2: The Computational Parts “We have to findContinue readingWhat I Read: What Color Is This? Part 2

What I Read: Reinforcement learning is supervised learning

By Andrew Fairless on February 2, 2021January 8, 2021

https://bair.berkeley.edu/blog/2020/10/13/supervised-rl/ Reinforcement learning is supervised learning on optimized dataBen Eysenbach and Aviral Kumar and Abhishek Gupta Oct 13, 2020 “This suggests that we can simply use inverse RL to relabelContinue readingWhat I Read: Reinforcement learning is supervised learning

What I Read: Software Tips for Data Science

By Andrew Fairless on February 2, 2021January 8, 2021

https://medium.com/swlh/software-engineering-tips-and-best-practices-for-data-science-5d85dbcf87fd Software Engineering Tips and Best Practices for Data ScienceWith great code comes great machine learningAhmed BesbesAug 28 “However, jupyter notebooks hide some serious pitfalls that may turn your codingContinue readingWhat I Read: Software Tips for Data Science

What I Read: Neural Networks Help Explain Brains

By Andrew Fairless on February 1, 2021January 7, 2021

https://www.quantamagazine.org/deep-neural-networks-help-to-explain-living-brains-20201028/ neuroscienceDeep Neural Networks Help to Explain Living Brains Deep neural networks, often criticized as “black boxes,” are helping neuroscientists understand the organization of living brains.“If their CNN mimicked aContinue readingWhat I Read: Neural Networks Help Explain Brains

Tag: machine learning