January 2021 – Page 6 – Andrew Fairless, Ph.D.

What I Read: Software engineering for Data Scientists

By Andrew Fairless on January 6, 2021December 22, 2020

https://towardsdatascience.com/software-engineering-fundamentals-for-data-scientists-6c95316d6cc4 Software engineering fundamentals for Data ScientistsThings I learnt not being a Software Engineer 🙃Gonzalo Ferreiro VolpiJun 4 “…we’ll see some of the fundamentals that I found useful the most,Continue readingWhat I Read: Software engineering for Data Scientists

What I Read: start deploying

By Andrew Fairless on January 6, 2021December 22, 2020

https://towardsdatascience.com/stop-training-more-models-start-deploying-them-using-webassembly-49a3f178569e Stop training more models, start deploying them (using WebAssembly)We are hardly living up to the promises of AI in healthcare. It’s not because of our training, it’s because ofContinue readingWhat I Read: start deploying

What I Read: Progress of Natural Language Processing

By Andrew Fairless on January 5, 2021December 22, 2020

https://blog.exxactcorp.com/the-unreasonable-progress-of-deep-neural-networks-in-natural-language-processing-nlp/ Deep LearningThe Unreasonable Progress of Deep Neural Networks in Natural Language Processing (NLP)Marketing, June 2, 2020 0 14 min read “With the advent of pre-trained generalized language models, weContinue readingWhat I Read: Progress of Natural Language Processing

What I Read: making machine learning actually useful

By Andrew Fairless on January 5, 2021December 22, 2020

https://www.shreya-shankar.com/making-ml-work/ Shreya ShankarReflecting on a year of making machine learning actually usefulJune 29, 2020 in #machine learning · 11 min read “…ML research as a field puts too much emphasisContinue readingWhat I Read: making machine learning actually useful

What I Read: Symbolic Models from Deep Learning

By Andrew Fairless on January 4, 2021December 22, 2020

https://astroautomata.com/paper/symbolic-neural-nets/ Discovering Symbolic Models from Deep Learning with Inductive BiasesPaper Code Interactive NotebookPaper authors: Miles Cranmer, Alvaro Sanchez-Gonzalez, Peter Battaglia, Rui Xu, Kyle Cranmer, David Spergel, Shirley Ho “…when doesContinue readingWhat I Read: Symbolic Models from Deep Learning

What I Read: Who Is Responsible When Autonomous Systems Fail?

By Andrew Fairless on January 4, 2021December 22, 2020

https://www.cigionline.org/articles/who-responsible-when-autonomous-systems-fail Who Is Responsible When Autonomous Systems Fail?Madeleine Clare ElishJune 15, 2020 “…conceptions of legal liability and responsibility did not adequately keep pace with advances in technology… while the controlContinue readingWhat I Read: Who Is Responsible When Autonomous Systems Fail?

What I Read: GPT-3, a Giant Step for NLP

By Andrew Fairless on January 3, 2021December 22, 2020

https://anotherdatum.com/gpt-3.html GPT-3, a Giant Step for Deep Learning And NLPPosted on 03 June 2020 “…OpenAI announced a new successor to their Language Model (LM) – GPT-3. This is the largestContinue readingWhat I Read: GPT-3, a Giant Step for NLP

What I Read: Computers Roll Loaded Dice

By Andrew Fairless on January 3, 2021December 22, 2020

https://www.quantamagazine.org/how-and-why-computers-roll-loaded-dice-20200708/ Abstractions blogHow and Why Computers Roll Loaded DiceResearchers are one step closer to injecting probability into deterministic machines.Stephen OrnesContributing WriterJuly 8, 2020 “FLDR won’t give you an edge inContinue readingWhat I Read: Computers Roll Loaded Dice

What I Read: Nitpicking ML Technical Debt

By Andrew Fairless on January 2, 2021December 22, 2020

https://matthewmcateer.me/blog/machine-learning-technical-debt/ Nitpicking Machine Learning Technical DebtRevisiting a resurging NeurIPS 2015 paper (and 25 best practices more relevant than that for 2020)May 10, 2020 “Now the Tech Debt Paper is makingContinue readingWhat I Read: Nitpicking ML Technical Debt

What I Read: Five Cognitive Biases

By Andrew Fairless on January 2, 2021December 22, 2020

https://builtin.com/data-science/cognitive-biases-data-science Five Cognitive Biases in Data Science (and How to Avoid Them)Everyone is prey to cognitive biases that skew thinking, but data scientists must prevent them from spoiling their work.RahulContinue readingWhat I Read: Five Cognitive Biases

Month: January 2021