almartin / Andrew

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Huffduffed (315)

  1. Seven Trends in Blockchain Computing (Spring 2019)

    In a follow-up to one of our most popular podcast episodes which originally aired in April 2017 (, a16z Crypto Fund General Partner Chris Dixon returns to talk with Olaf Carlson-Wee of Polychain Capital in a free-wheeling conversation about the seven major trends they see happening in blockchain computing now as we shift from basic protocol design to pragmatic product launches:

    • Improving developer productivity
    • Scaling out versus scaling up
    • On-chain governance
    • Proof of Stake Networks, and especially their resilience to attacks
    • 2017: year of of fund raising, 2019: year of launches
    • Autonomous and re-mixable code
    • Killer apps: distributed finance and beyond

    The views expressed here are those of the individual AH Capital Management, L.L.C. (“a16z”) personnel quoted and are not the views of a16z or its affiliates. Certain information contained in here has been obtained from third-party sources, including from portfolio companies of funds managed by a16z. While taken from sources believed to be reliable, a16z has not independently verified such information and makes no representations about the enduring accuracy of the information or its appropriateness for a given situation.

    This content is provided for informational p…

    Original video:
    Downloaded by on Wed Aug 18 14:02:31 2021 Available for 30 days after download

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  2. Luigi Patruno - Machine Learning in Production - 6/30/2021

    Luigi Patruno joins the Data Nerd Herd to discuss his path into machine learning, his newsletter Machine Learning in Production, transitioning to a manager role, and mma, jiu jitsu, and other awesome stuff.


    #mlops #mlengineering #data #ai #machinelearning —————————————-

    About Luigi Patruno

    Luigi is a data scientist and the founder of ML in Production. His goal is to help educate data scientists/analysts/engineers about best practices for running machine learning systems in production. He's also the Senior Director of Data Science at 2U.

    LinkedIn: Machine Learning in Production:

    —————————————- TERNARY DATA

    Ternary Data is not your typical data consultancy. Get no-nonsense, no BS cloud data strategy, coaching, and advice. Trusted by great companies, both huge and small.

    🚨 Visit our website to learn more and to work with us: Connect with us on LinkedIn:

    🔊 Want the Monday Morning Data Chat podcast? Get it here:

    ➕ SUBSCRIBE for more content like this! Like the video if you gained something from it and comment any thoughts or questions you may have! We look forward to connecting with you.

    Original video:
    Downloaded by on Wed Jul 7 13:38:01 2021 Available for 30 days after download

    Tagged with entertainment

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  3. Data Labeling as Religious Experience

    Data Labeling as Religious Experience Josh Wills, Slack Presented at MLconf San Francisco 2019

    Abstract: One of the most common places to deploy a production machine learning systems is as a replacement for a legacy rules-based system that is having a hard time keeping up with new edge cases and requirements. I’ll be walking through the process and tooling we used to help us design, train, and deploy a model to replace a set of static rules we had for handling invite spam at Slack, talk about what we learned, and discuss some problems to solve in order to make these migrations easier for everyone.

    See Josh's presentation slides on our slideshare page here:

    Original video:
    Downloaded by on Fri Dec 11 20:32:01 2020 Available for 30 days after download

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  4. Competition Winning Learning Rates

    Leslie Smith, Senior Research Scientist, US Naval Research Laboratory Presented at MLconf 2018 Abstract: It is well known that learning rates are the most important hyper-parameter to tune for training deep neural networks. Surprisingly, training with dynamic learning rates can lead to an order of magnitude speedup in training time. This talk will discuss my path from static learning rates to dynamic cyclical learning rates and finally to fast training with very large learning rates (I named this technique “super-convergence”). In particular, I will show that very large learning rates are the preferred method for regularizing the training because they provide the twin benefits of training speed and good generalization. The super-convergence method was integrated into the library and the Fastai team used it to win the DAWNBench and Kaggle’s iMaterialist challenges.

    See Leslie's presentation slides on our slideshare page here:

    Original video:
    Downloaded by on Fri Dec 11 20:32:02 2020 Available for 30 days after download

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  5. Path To Financial Identity w/ Jason Robinson, VP of Product @ Juvo

    Juvo establishes financial identities for the billions of people worldwide who are creditworthy, yet financially excluded.

    From our 2018 Fintech Inclusion Summit.

    Check out our website for more details and upcoming events.

    Original video:
    Downloaded by on Fri Nov 20 14:52:36 2020 Available for 30 days after download

    —Huffduffed by almartin

  6. Product Lessons w/ Adam Nash

    From our July Meetup Join Here — Adam shares hard won product lessons learned over his career, during which he has worked alternately as an engineer, product manager and executive. He is currently serving as on the board of Acorns and as an Entrepreneur in Residence at Greylock. He was formerly the CEO at Wealthfront.

    Original video:
    Downloaded by on Fri Nov 20 14:52:43 2020 Available for 30 days after download

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