Author: Andrew Fairless

What I Read: Graph Neural Networks, Differential Geometry, Algebraic Topology

https://towardsdatascience.com/graph-neural-networks-through-the-lens-of-differential-geometry-and-algebraic-topology-3a7c3c22d5f Graph Neural Networks through the lens of Differential Geometry and Algebraic TopologyMichael Bronstein “Differential geometry and algebraic topology are not encountered very frequently in mainstream machine learning… tools fromContinue readingWhat I Read: Graph Neural Networks, Differential Geometry, Algebraic Topology

What I Read: Improving a Machine Learning System (Part 1 – Broken Abstractions)

https://danshiebler.com/2021-11-06-ml-systems-1/ Improving a Machine Learning System (Part 1 – Broken Abstractions)Dan ShieblerPosted on November 6, 2021 “Making measurable improvements to a mature machine learning system is extremely difficult. In thisContinue readingWhat I Read: Improving a Machine Learning System (Part 1 – Broken Abstractions)

What I Read: From Data Engineer to SysAdmin: Put down the K8s cluster

https://thundergolfer.com/kubernetes/infrastructure/data-engineering/2021/11/04/from-data-eng-to-sys-admin-put-down-k8s/ From Data Engineer to SysAdmin: Put down the K8s cluster, your pipelines can run without itJonathon BelottiNov 4, 2021 “Now this is like the thousandth ‘beware K8s’ post, andContinue readingWhat I Read: From Data Engineer to SysAdmin: Put down the K8s cluster

What I Read: Deep Learning Optimization Theory

https://towardsdatascience.com/deep-learning-optimization-theory-introduction-148b3504b20f?gi=ff0bd10cc9fe Deep Learning Optimization Theory — IntroductionUnderstanding the theory of optimization in deep learning is crucial to enable progress. This post introduces the experimental and theoretical approaches to studying it.OmriContinue readingWhat I Read: Deep Learning Optimization Theory