Certain technologies like scikit-learn are foundational to an entire generation as suggested by Logan Kilpatrick, President of NumFOCUS. If it is in fact the backbone, we could in fact suggest that scikit-learn is an integral part of the AI infrastructure layer. Source: Logan Kilpatrick Want to make your own scikit-learn #appraisal and help us spread the love? Fill in this form: https://lnkd.in/eB2V6hWN :probabl. is recruiting, check our open positions: https://lnkd.in/eUJ6UWiM #OwnYourDataScience
À propos
Probabilities are at the core of every decision we make, never quite perfect, often incomplete. In fact, our view of the world’s digital twin is made of many digits with floating points, neither black and white nor binary, mostly filled with deep gradients. Knowledge is at your fingertips, yet trapped inside. Once the data model fits, because we can explain, you can therefore infer and act. Our brand, our logo, the three dots… it’s all in there. Welcome to :probabl.
- Site web
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https://probabl.ai
Lien externe pour :probabl.
- Secteur
- Développement de logiciels
- Taille de l’entreprise
- 11-50 employés
- Siège social
- Paris
- Type
- Société civile/Société commerciale/Autres types de sociétés
- Fondée en
- 2023
- Domaines
- data science, machine learning et open source
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Berlin, DE
Employés chez :probabl.
Nouvelles
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Seasonal patterns really matter when you want to do long term timeseries prediction. The good news? Scikit learn has plenty of components that help you do precisely this. https://lnkd.in/eCWH8SqT
Generating Periodic Features for Seasonal Timeseries
https://www.youtube.com/
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While it helps to give missing values your attention, you can don't always have to worry so much about them. Many models these days have a pretty robust way of dealing with it. Our latest whiteboard video has all the details: https://lnkd.in/envmg6Cy
Don't worry too much about missing data
https://www.youtube.com/
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We're looking forward to actively participate in PyData Paris with our friends from QuantStack. See you in September!
⭐ We proudly welcome :probabl. as gold sponsor for PyData Paris 2024. 🚀 Thank you for supporting the PyData community and for the help with the organization. 🎟 Buy your tickets at https://lnkd.in/epb9GFGC and join us on September 25-26 at Cité des sciences et de l'industrie.
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One of our colleages, Vincent, will make an appearance on the Vanishing Gradients podcast next week and he may show a new and fun scikit-learn related demo ... he may even show two. Tune in live here if you're interested: https://lnkd.in/eswkqAiA
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We have a new podcast episode! This one is with our very own Adrin Jalali. He's well known for his work in scikit-learn but also for his recent efforts in skops, which tries to make it safer to store and load ML models. A lot of fun topics came up in this one, we hope you like it! https://lnkd.in/earuyVMz
Model safety, that's a pickle! with Adrin Jalali - scikit-learn maintainer
https://www.youtube.com/
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If you're curious about lessons learned from maintaing scikit-learn, you might enjoy tuning in to this livestream. Our very own Guillame will be the guest and will also discuss his work on imbalanced-learn. https://lnkd.in/esspjdjP
Working as a Core Developer in the Scikit Learn Universe · Luma
lu.ma
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There are times when supervised learning simply won't cut it. But what if we do semi-supervised instead? https://lnkd.in/e66Vne6a
Boosting vs. semi-supervised learning
https://www.youtube.com/
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Vincent will go live later today to show some recent features in Sentence-Transformers v3. If that sounds like fun to you, feel free to join in live over here: https://lnkd.in/ex7Ey4yp
Sentence-Transformers v3
https://www.youtube.com/
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Boosted models can overfit, so you probably want to double-check your hyperparameters. But there is more than meets the eye once you go there. https://lnkd.in/eDudtTFh
Benchmarking boosted trees against overfitting
https://www.youtube.com/