How does Replit leverage MosaicML? @YamiToughCookie
MACHINE LEARNING
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Model shape importance beyond size in AI architecture
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@pirroh @jefrankle Why should model shape be considered in addition to model size?
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Large Language Models Applied to DNA, Proteins, Biological Data
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@NEJM a tour de force review
Large "language" #AI models aren't just for words–they can be used for DNA, proteins and all the biologic dimensions of data https://
nejm.org/doi/full/10.10
56/NEJMra2204787?query=featured_home
… by @euanashley and Bruna Gomes -
Embracing AI While Maintaining Professional Integrity and Ethics
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Embracing AI Without Compromising Your Integrity
#AI #AIio #BigData #ML #NLU #Futureofwork http://
ow.ly/5ZBF30svSeB -

Abacus AI: Complete ML Pipeline from Data to Production Monitoring
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The future is here! You can talk to our AI @abacusai and tell it to: 1. Clean your data
2. Create ML features
3. Train and deploy models
4. Monitor these models in production This is incredibly helpful for data scientists building systems. -
Machine Learning in Marketing: AI Applications and Strategies
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Introducción al Machine Learning para el Marketing con Inteligencia Artificial #podcast
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ChatGPT Cheat Sheet for Machine Learning Tasks
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In this cheat sheet, learn how to use ChatGPT to assist with the following machine learning tasks: Project Planning
Feature Engineering
Data Preprocessing
Model Selection
Hyperparameter Tuning
Experiment Tracking
MLOps Cheat Sheet https://
bit.ly/3CFhz1i -
SLM Acronym Ambiguity: Small Language Model or Subspace Learning Machine
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SLM as in Small Language Model or Subspace Learning Machine?
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Training Models to Convergence Remains Essential
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The day where I/we can’t train a model until convergence anymore will be a sad day.
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Fourier Neural Operators Achieve 10^4-10^5 Speedups Weather Forecasting
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Thank you @HPCsharp @PASC_Conference https://
arxiv.org/abs/2208.05419 Our work was first to do weather forecasts at 25km spatial resolution with 10^4-10^5 speedups using Fourier neural operators. Recently we added spherical harmonics for stable long rollouts https://
arxiv.org/abs/2306.03838