2022 – Page 12 – Andrew Fairless, Ph.D.

What I Read: Visual Explanation of Classifiers

By Andrew Fairless on March 23, 2022February 3, 2022

https://ai.googleblog.com/2022/01/introducing-stylex-new-approach-for.html Introducing StylEx: A New Approach for Visual Explanation of ClassifiersTuesday, January 18, 2022Posted by Oran Lang and Inbar Mosseri, Software Engineers, Google Research“Previous approaches for visual explanations of classifiers…Continue readingWhat I Read: Visual Explanation of Classifiers

What I Read: Principles that Boost Innovation

By Andrew Fairless on March 22, 2022February 3, 2022

https://www.pymc-labs.io/blog-posts/labs-principles/ My Journey Building PyMC Labs: Five Principles from Open Source that Boost Innovation at any Company5 organizational principles that foster innovation at PyMC LabsAuthored by Thomas Wiecki on 2022-01-10“IContinue readingWhat I Read: Principles that Boost Innovation

What I Read: ML model drift in production

By Andrew Fairless on March 21, 2022January 20, 2022

https://towardsdatascience.com/my-data-drifted-whats-next-how-to-handle-ml-model-drift-in-production-78719ef007b1 “My data drifted. What’s next?” How to handle ML model drift in production.An introductory overview of the possible steps.Elena SamuylovaDec 1, 2021 ““I have a model in production, andContinue readingWhat I Read: ML model drift in production

What I Read: Aristotle, Deep Learning

By Andrew Fairless on March 17, 2022March 12, 2022

https://thegradient.pub/how-aristotle-is-fixing-deep-learnings-flaws/ How Aristotle is Fixing Deep Learning’s FlawsPaul J. Blazek17.Feb.2022 “…Aristotle’s logic still stands strong, poignantly describing the building blocks of human reasoning. Yet many of the key ingredients describedContinue readingWhat I Read: Aristotle, Deep Learning

What I Watch: Engineering For Data

By Andrew Fairless on March 16, 2022January 20, 2022

https://youtu.be/jeMQld-6BtQ What Is Engineering For Data?Jan 5, 2022Continuous Delivery “What are the big mistakes that data scientists and big organisations often make when dealing with big data? What does engineeringContinue readingWhat I Watch: Engineering For Data

What I Read: marketplace ranking infrastructure

By Andrew Fairless on March 15, 2022January 20, 2022

https://craft.faire.com/building-faires-new-marketplace-ranking-infrastructure-a53bf938aba0 Building Faire’s new marketplace ranking infrastructureSam KennyNov 3, 2021 “At Faire, all three of these core surfaces are powered by one unified ranking infrastructure that we refer to internallyContinue readingWhat I Read: marketplace ranking infrastructure

What I Read: evaluate online machine learning models

By Andrew Fairless on March 14, 2022January 20, 2022

https://maxhalford.github.io/blog/online-learning-evaluation/ The correct way to evaluate online machine learning modelsMax Halford2020-06-07 “Batch models are meant to be used when you can afford to retrain your model from scratch every soContinue readingWhat I Read: evaluate online machine learning models

What I Read: Real-time machine learning

By Andrew Fairless on March 9, 2022January 20, 2022

https://huyenchip.com//2022/01/02/real-time-machine-learning-challenges-and-solutions.html Real-time machine learning: challenges and solutionsJan 2, 2022 • Chip Huyen “Starting with a batch prediction system, we’ll discuss what’s needed for a simple online prediction system using batchContinue readingWhat I Read: Real-time machine learning

What I Read: Engineering Trade-Offs in Automatic Differentiation

By Andrew Fairless on March 8, 2022January 20, 2022

https://www.stochasticlifestyle.com/engineering-trade-offs-in-automatic-differentiation-from-tensorflow-and-pytorch-to-jax-and-julia/ Engineering Trade-Offs in Automatic Differentiation: from TensorFlow and PyTorch to Jax and JuliaDecember 25 2021Author: Christopher Rackauckas “To understand the differences between automatic differentiation libraries, let’s talk about theContinue readingWhat I Read: Engineering Trade-Offs in Automatic Differentiation

What I Read: Generative Modeling by Estimating Gradients

By Andrew Fairless on March 7, 2022January 20, 2022

https://yang-song.github.io/blog/2021/score/ Generative Modeling by Estimating Gradients of the Data DistributionAuthor Yang SongDate May 5, 2021 “This blog post gives a detailed introduction to score-based generative models. We demonstrate that thisContinue readingWhat I Read: Generative Modeling by Estimating Gradients

Year: 2022