Christopher #AlwaysBeLearning Poptic

@ChrisPoptic

Data scientist, machine learning eng, bike racer. "Here I stand, atoms with consciousness, matter with curiosity. A universe of atoms, an atom in the universe."

ENTP / ENTJ
Vrijeme pridruživanja: svibanj 2009.

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  1. Prikvačeni tweet

    "...bear in mind that every man lives only this present time, which is an indivisible point, and that all the rest of his life is either past or it is uncertain. Short then is the time which every man lives, and small the nook of the earth where he lives; and short too the

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  2. proslijedio/la je Tweet
    4. velj

    "How to do machine learning efficiently". There's so much to love about this wonderful article.

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  3. proslijedio/la je Tweet
    2. velj
    Odgovor korisniku/ci

    It’s always so amazing to me when folks continually argue for their limited beliefs. Think with a limited mindset and it becomes your truth, which also means we can flip that around and focus on abundance...

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  5. proslijedio/la je Tweet
    29. sij

    Remarkable. Electronic patient records systems used by thousands of doctors were programmed to automatically suggest opioids at treatment, thanks to a secret deal between the software maker and a drug company. reports. via

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  6. proslijedio/la je Tweet
    15. kol 2019.

    "New State of the Art AI Optimizer: Rectified Adam (RAdam). Improve your AI accuracy instantly versus Adam, & why it works" It's been a long time since we've seen a new optimizer reliably beat the old favorites; this looks like a very encouraging approach!

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  7. Really enjoyed this episode. Workflow design and data pipeline automation of production workflows is a major challenge in production-scale machine learning projects. Great to see how Lyft is handling these challenges.

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  8. This is hilarious because of how accurate it is. I see opportunity here for a fork of Jupyter notebook that preserves ther original execution order of one's notebook. Immutable cell execution order... Wait, blockchain for Jupyter! Project name: ....Pepe Silvia

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  9. proslijedio/la je Tweet
    26. sij
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  10. proslijedio/la je Tweet

    You can become successful at something without knowing what you're doing. You can become successful at something without having much particular talent at it. But you can never become successful at anything without taking action. Ever.

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  11. proslijedio/la je Tweet
    24. sij

    I have thought about these two paragraphs so many times over the years. Huge thanks to for finding the source.

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  12. proslijedio/la je Tweet
    23. sij

    The January DVC Heartbeat is here. In this issue we're discussing , reaching 100 contributors, reducing technical debt, and learning about MLOps.

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  13. proslijedio/la je Tweet
    28. lip 2019.
    Odgovor korisniku/ci

    I think so, yes. VI when you mostly care about prediction (e.g. ) and sampling when you care about inference.

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  14. proslijedio/la je Tweet
    16. lis 2019.

    Sage advice that maps easily to any Machine Learning product - start by deploying the simplest possible ‘toy model’ into production and then iterate

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  15. One trait I've observed important in software development is persistence and scrappiness. So much of working in tech is problem-solving. And not just the problem your project is trying to solve, but also all the bugs/inconsistencies/config issues that inevitably arise during it.

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  16. proslijedio/la je Tweet
    13. sij

    I published a new article on the blog: Active Transfer Learning with PyTorch. Read about adapting Machine learning models with the knowledge that some data points will later get correct human labels, even if the model doesn't yet know the labels:

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  17. proslijedio/la je Tweet
    14. sij

    Learn how to automate most of the infrastructure work required to deploy PyTorch models in production using Cortex, an open source tool for deploying models as APIs on AWS.

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  18. 3 — Usually outperform Adam in final results due to improved stability throughout training (via the longer term memory and clipping from it). Excited to implementing this new SOTA optimizer in future deep learning training.

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  19. AdaMod (Adam + memory) represents another step forward for deep learning optimizers as it provides three improvements: 1 — No need for warm-up (similar to Rectified Adam) 2 — Reduced sensitivity to learning rate hyperparam (converges to similar results)

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  20. proslijedio/la je Tweet
    22. pro 2019.
    Odgovor korisnicima

    Dear Cherry, Please retract the ACM endorsement of this letter. The ACM should be devoted to the open exchange of scientific information. The letter is an attempt to preserve the undue profiteering of commercial publishers.

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  21. proslijedio/la je Tweet
    12. sij

    Working with nbdev () is such a great experience! Finally breaking the cycle of starting to code in notebooks, then exporting the code to a library, and finally writing documentation. Awesome job & !

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