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
Lieux
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Principal
Montparnasse
Paris, FR
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Berlin, DE
Employés chez :probabl.
Nouvelles
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Going live in a bit! https://lnkd.in/e_tGBJgr
UMAP vs. PCA
https://www.youtube.com/
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Just as an extra reminder, the livestream is back! If you're keen on seeing another fun demo live tomorrow during lunch you are more than welcome to tune in here: https://lnkd.in/eBYc8FN9
UMAP vs. PCA
https://www.youtube.com/
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Histograms are neat for visualisations, but numeric systems may benefit from something more precise. After all ... if you can estimate a distribution, a small world of applications opens up! Our latest video gives a demo: https://lnkd.in/gRSf5dxu
Understanding how the KernelDensityEstimator works
https://www.youtube.com/
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The livestreams are back! In the next one Vincent will dive into the differences between PCA and UMAP. The emphasis is on intuition, not maths, and you can expect some fun interactive widgets too! You can watch live this weekend here: https://lnkd.in/eHdG22Ji
UMAP vs. PCA
https://www.youtube.com/
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Doctors use checklists before they do surgery because it prevents a lot of failures. What if we did the same in Machine Learning? To help answer this question we've interviewed Peter Bull from Drivendata on our podcast, enjoy! https://lnkd.in/eScNHwjt
Pragmatic data science checklists with Peter Bull
https://www.youtube.com/
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When you are looking for the best hyperparameters, you nearly always want to look randomly in the search space. There is however, one ... very interesting ... caveat. https://lnkd.in/eG5MXhg3
Random Search is better, but there is one caveat
https://www.youtube.com/
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It turns out ... PCA can be used for *a lot of fun* too! https://lnkd.in/gHm-Phnb
Use-cases for inverted PCA
https://www.youtube.com/
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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/