Ben Lorica 罗瑞卡

@bigdata

Helping organize & ; Past: Program Chair of & & . Host of The Data Exchange podcast.

San Francisco, CA
Vrijeme pridruživanja: prosinac 2008.

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  1. Prikvačeni tweet
    30. sij

    What Is a Data Lakehouse? 🆕 post on a data management paradigm for the age of and (written with some of the founders and )

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  2. prije 22 sata

    In this episode of I speak with , research scientist and creator of 📻 He gives a great overview of tools and techniques for privacy-preserving

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  3. 31. sij

    Call For Speakers for the first closes TODAY 🕛 January 31st 🔜 We are putting together an outstanding program: confirmed speakers include , Manuela Veloso of & more

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  4. proslijedio/la je Tweet
    31. sij

    Simon Mo from discussing the new Serve library

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  5. 31. sij

    Sighting meetup in San Francisco 👉 Paco P = Python A = AI C = Cloud O =Operations

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  6. proslijedio/la je Tweet
    30. sij

    Learn how a and are now being succeeded by the data management paradigm to unify and simplify business analytics and machine learning. co-written by

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  7. proslijedio/la je Tweet
    24. sij

    I’m proud of what we have accomplished together with data in 2019: The millions of VMs and exabytes of data per day contributed to important social issues (energy, diseases, market manipulation). Here’s a recap of 2019:

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  8. 30. sij

    4/ If you are needing a great intro to privacy-preserving analytics and , I recommend you listen to my conversation with . If you have feedback or suggestions for us, fill out the contact form on site /End

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  9. 30. sij

    3/ It’s clear that privacy-preserving ML solutions will employ a variety of techniques including cryptography, homomorphic encryption, federated learning, secure aggregation, differential privacy, MPC and more. We discussed these & ’s stack for coopetitive learning (MC2)

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  10. 30. sij

    2/ Morten described how began, the current state of the project, and how it fits into the broader space of privacy-preserving analytics and machine learning. He notes that current solutions are still too slow for many (near) real-time applications.

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  11. 30. sij

    Thread: In this episode of I speak with , research scientist and creator of . We began by discussing how he found himself at the intersection of security and machine learning

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  12. 29. sij

    Call For Speakers for the first closes this Friday 🕛 January 31st 🔜 We are putting together an outstanding program: confirmed speakers include , Manuela Veloso of & more

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  13. 29. sij

    Ray Tips and Tricks — If you are interested in scaling your or applications, this will be a great series of posts on from

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  14. 29. sij

    "We expect to recommend Ray for distributed training. offers a clean and simple API that fits well with Thinc’s model design."

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  15. proslijedio/la je Tweet

    The Exchange podcast comes back this week with an excellent deep dive into the key AI, Machine Learning and data trends for 2020. In this episode they dive into types of machine learning, real life applications, infrastructure/tools and more.

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  16. 29. sij

    "Thinc is a lightweight deep learning library that offers an elegant, type-checked, functional-programming API for composing models, with support for layers defined in other frameworks such as PyTorch, TensorFlow or MXNet" from Team

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  17. proslijedio/la je Tweet
    28. sij

    "Taking messaging and data ingestion systems to the next level" - Our co-founder and CEO chatted with in about data ingestion and for modern data and applications. Don't miss it!

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  18. 28. sij
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  19. proslijedio/la je Tweet
    27. sij

    Apache Pulsar crossed 5K stars today. Excited to see the community growth and increased adoption!

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  20. 26. sij

    PyHessian from is a new scalable framework that enables fast computation of Hessian information for deep neural networks: "by computing finer-scale Hessian-based statistics, we show that in some cases conventional understanding ... is not correct"

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