Infographic: #DataScience Deconstruction
Via @ingliguori #coding #programming #python #javascript #Engineering #AI #DataScientist #BigData #MachineLearning #Analytics #RStats #Innovation #selfdrivingcars #IoT #IIoT #smartcity #100DaysofCode #cx #fashiontech #5G #web3 #DevOps
MACHINE LEARNING
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Data Science Deconstruction Infographic Overview
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ML Engineering: Master Semantics Through Self-Contained Test Files
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ML engineering productivity hack: write small self-contained files testing your understanding of any semantics you're fuzzy about (e.g. how CUDA streams interact with a full CUDA queue). Goal should be to turn these semantics into "muscle memory" before working end-to-end.
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Testing Model Guidance Levels Finding Optimal Sweet Spot
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randomly had this page open so tried it on the published model. guidances at 7, 10, and 13 from bottom to top. looks like 10 is sweet spot
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Free Machine Learning Crash Course from Berkeley ML
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Free machine learning crash course from @BerkeleyML
. Highly recommended for anyone looking to start! https://
ml.berkeley.edu/blog/tag/crash
-course/
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Machine Learning Automation Lets Humans Relax Worry
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Let the machine learn, so humans can chill (probably?) Image source: Google
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IBM Research Introduces FETA Benchmark for AI Technical Documentation Reading
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.@IBMResearch introduces FETA (Foundation models for Expert Task Applications) at #NeurIPS2022 — a first-of-its-kind benchmark meant to measure how well an #AI can read complex technical documentation: https://t.co/mGh0m1oR0z
— IBM Data, AI & Automation (@IBMData) 23 décembre 2022
—–#IBM #opensource #ML #datascience #automation.
@IBMResearch introduces FETA (Foundation models for Expert Task Applications) at #NeurIPS2022 — a first-of-its-kind benchmark meant to measure how well an #AI can read complex technical documentation: https://
ibm.co/3WCz363 —–
#IBM #opensource #ML #datascience #automation -
Thunderstruck: Metanomic’s AI Player Analysis Platform
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Focus on Metanomic's player analysis platform "Thunderstruck" https://actuia.com/actualite/focus-sur-la-plateforme-danalyse-des-joueurs-thunderstruck-de-metanomic/
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Google’s AI Model Announcements Lag Behind Meta and OpenAI
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True, honestly, IDK why they are running behind in the industry. since BERT they haven't announced any significant model release. while even meta, openai are moving really fast, even though google has more access to the world's data than any other org.
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Unavoidable difficulty of logprobs for completion sequences in classification
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The number of logprobs returned per token isn’t the issue here, it’s the unavoidable difficulty of getting logprobs for different completion sequences. In a classification context you need to assign class labels to individual tokens during training.