Andrew Fairless – Page 42 – Andrew Fairless, Ph.D.

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

What I Read: AI Accelerators, Very Rich Landscape

By Andrew Fairless on March 2, 2022January 20, 2022

https://medium.com/@adi.fu7/ai-accelerators-part-iv-the-very-rich-landscape-17481be80917 AI Accelerators — Part IV: The Very Rich LandscapeAdi FuchsDec 5 “In this article, we will go over the current state of the AI hardware industry and present anContinue readingWhat I Read: AI Accelerators, Very Rich Landscape

What I Read: AI Accelerators, Architectural Foundations

By Andrew Fairless on March 1, 2022January 20, 2022

https://medium.com/@adi.fu7/ai-accelerators-part-iii-architectural-foundations-3f1f73d61f1f AI Accelerators — Part III: Architectural FoundationsAdi FuchsDec 5 “Many different companies build AI accelerators. The landscape is filled with an abundance of ideas and assumptions based on theContinue readingWhat I Read: AI Accelerators, Architectural Foundations

What I Read: AI Accelerators

By Andrew Fairless on February 28, 2022January 20, 2022

https://medium.com/@adi.fu7/ai-accelerators-part-ii-transistors-and-pizza-or-why-do-we-need-accelerators-75738642fdaa AI Accelerators — Part II: Transistors and Pizza (or: Why Do We Need Accelerators)?Adi FuchsDec 5 “There can not be a “one size fits all” approach when it comesContinue readingWhat I Read: AI Accelerators

What I Read: How Should Organizations Structure their Data?

By Andrew Fairless on February 23, 2022January 20, 2022

https://towardsdatascience.com/how-should-organizations-structure-their-data-c19b66d629e How Should Organizations Structure their Data?A review of three popular data warehousing techniquesMichael Berk “Efficient data infrastructure leads to efficient data scientists. How you structure your data as anContinue readingWhat I Read: How Should Organizations Structure their Data?

Author: Andrew Fairless