machine learning – Page 28 – Andrew Fairless, Ph.D.

What I Read: Textless NLP

By Andrew Fairless on April 13, 2022March 12, 2022

https://ai.facebook.com/blog/textless-nlp-generating-expressive-speech-from-raw-audio Textless NLP: Generating expressive speech from raw audioSeptember 9, 2021 “GSLM leverages recent breakthroughs in representation learning, allowing it to work directly from only raw audio signals… for potentiallyContinue readingWhat I Read: Textless NLP

What I Read: Why Bigger Neural Networks Do Better

By Andrew Fairless on April 11, 2022March 12, 2022

https://www.quantamagazine.org/computer-scientists-prove-why-bigger-neural-networks-do-better-20220210/ Computer Scientists Prove Why Bigger Neural Networks Do BetterMordechai RorvigStaff WriterFebruary 10, 2022 “Two researchers show that for neural networks to be able to remember better, they need farContinue readingWhat I Read: Why Bigger Neural Networks Do Better

What I Read: Data Distribution Shifts

By Andrew Fairless on April 6, 2022March 12, 2022

https://huyenchip.com//2022/02/07/data-distribution-shifts-and-monitoring.html Data Distribution Shifts and MonitoringChip HuyenFebruary 7 2022 12:00 AM “…deploying a model isn’t the end of the process. A model’s performance degrades over time in production. Once aContinue readingWhat I Read: Data Distribution Shifts

What I Read: Scale Real-time Data Infrastructure

By Andrew Fairless on April 4, 2022February 3, 2022

https://zhenzhongxu.com/the-four-innovation-phases-of-netflixs-trillions-scale-real-time-data-infrastructure-2370938d7f01 The Four Innovation Phases of Netflix’s Trillions Scale Real-time Data InfrastructureZhenzhong Xu2/1/2022“I think it’s a perfect time to summarize my learnings building the real-time data infrastructure at Netflix. IContinue readingWhat I Read: Scale Real-time Data Infrastructure

What I Read: Researchers Build AI That Builds AI

By Andrew Fairless on March 30, 2022February 3, 2022

https://www.quantamagazine.org/researchers-build-ai-that-builds-ai-20220125/ Researchers Build AI That Builds AIAnil AnanthaswamyJanuary 25, 2022“By using hypernetworks, researchers can now preemptively fine-tune artificial neural networks, saving some of the time and expense of training.”

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: 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: 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

Tag: machine learning