Python is removing the GIL.
The GIL (Global Interpreter Lock) prevents you from running multi-threaded code.
That makes ML code, in particular, really hard to write in pure Python.
Here's what it takes to remove the GIL:
O Processo Seletivo da UFG contará com vagas especiais pessoas que fizeram o Enem entre os anos de 2009 a 2022. No bacharelado de Inteligência Artificial são 3 vagas de ampla concorrência e 4 vagas de cotas.
Maiores informações: https://t.co/QibbQGGk21
Estamos com uma vaga incrível para Staff Data Scientist no PicPay.
O foco dessa vaga é uma pessoa com experiência em information retrieval e/ou engenharia de busca.
Se você tem experiência em ML aplicada e engenharia de software esse pode ser um bom desafio 💚
Link na thread:
A book I've been looking forward to for a long time is out: The Staff Engineer's Path by @whereistanya.
Tanya was kind enough to share a chapter from the book, and you can read it here: https://t.co/55qmLpECAd
Do you want to know the difference between the recommendation problem and the advertising problem, especially from the perspective of Reinforcement Learning?
I highly recommend this episode of Recsperts, with @LivesInAnalogia and @olivierjeunen
⚡️Preprint: Why do tree-based models still outperform deep learning on tabular data?
We give solid evidence that, on tabular data, achieving good prediction is easier with tree methods than deep learning (even modern architectures) and explore why
https://t.co/qft4LgZm0z
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As the MLOps ecosystem continues to grow there is a need for a taxonomy to classify the different tools and frameworks in the field. This IEEE paper encompasses a taxonomy and methodology to exactly this challenge with a comprehensive overview https://t.co/jzGLrmX24T
I am very pleased to share that “Transformers are Meta-Reinforcement Learners” is being published as part of the International Conference on Machine Learning (ICML) 2022!
Paper: https://t.co/3drltoYWZv
Interested in knowing more about the work? Follow the 🧵: [1/N]
ML bugs are so much trickier than bugs in traditional software because rather than getting an error, you get degraded performance (and it's not obvious a priori what ideal performance is).
So ML debugging works by continual sanity checking, e.g. comparing to various baselines.
Novo episódio no ar!
Conversamos sobre o complexo mundo da causalidade com Marcel Ribeiro-Dantas (@mribeirodantas), pesquisador e especialista na área.
áudio: https://t.co/rDO6LzENvE
vídeo: https://t.co/wvz0vcRdyV
Tem vídeo novo no Youtube! Nesse episódio, Paulo Vasconcellos (@paulo_zip ) conta a verdade sobre quanto tempo demora para se tornar um Cientista de Dados
https://t.co/JomU8F9RW6
Gente, orgulho de ver o #Magalu entre as empresas brasileiras mais admiradas pelos profissionais de dados, segundo a pesquisa State Of Data, do @datahackersofic, uma das principais comunidades de data science do país! Parabéns ao nosso time! https://t.co/q0b08CN7a2
Fechamos a semana com a conquista nacional da nossa Equipe de Processamento de Voz.
Com nossa equipe júnior, conquistamos o 1° Lugar 🏆do Ranking de Análise de Sentimento. Ganhamos 03 das 04 tarefas propostas.
NEW InClass @kaggle competition launched! Help a public Brazilian university deploy its #AI models to carry out AI validation by sorting body parts on X-rays. @unifesp@ddiunifesp@UNIFESP_Rad#dasa
https://t.co/PxNlikYy0s
Updated version of the modern data stack architecture. Put together after tons of diligence work what what tech / products are actually being used in practice.
https://t.co/6KD4YaNlJW