RAPIDSFire

RAPIDS
  • 4.9(13개의 평가)
  • IT 뉴스

Follow the RAPIDSFire podcast for a fresh take on data science. Hear from revolutionaries transforming data science on GPUs for scientific research, higher education, and the broader enterprise. Talks with open-source software maintainers, Kaggle grandmasters, practitioners, CUDA experts and many others keep you up-to-date on the most exciting developments. Let's discuss how to make your work better and faster. Hosted by Data Scientist Paul Mahler. Join the conversation and send feedback on Twitter @rapidsai

  1. 2021. 10. 14.

    RAPIDSFire Sports Spectacular 1 - Sam Moss and Cameron Weinert of Every Day is Saturday

    I talk with Sam Moss and Cameron Weinert about using data science to predict college football. We talk about feature engineering, following your passing, the role of analytics in sports and sports fandom, how to be an intelligent consumer of data science as a non-data scientist, and a lot more.  Everyday is Saturday on Spotify.  Everyday is Saturday on Apple.  Sam on Twitter.  Raw data on college football here at collegefootballdata.com

    46분

  2. 2021. 10. 05.

    Marlene Mhangami on Python, Pivots, and Personal Growth (and RAPIDS on Windows)

    We talk with Marlene Mhangami, a director and chair of the Python Software Foundation, co-founder of coding education non-profit ZimboPy, someone that took a huge career pivot from pre-med to software engineering, and one of the folks that helped bring RAPIDS to Windows. We talk about changing careers, creativity and confidence in tech, and of course RAPIDS on Windows.  Marlene's home page https://marlenemhangami.com/ Marlene's blog post about RAPIDS on Windows https://medium.com/rapids-ai/running-rapids-on-microsoft-windows-10-using-wsl-2-the-windows-subsystem-for-linux-c5cbb2c56e04 Tutorial on using RAPIDS on Windows via WSL2 https://www.youtube.com/watch?v=jnEd3IDsF-I ZimboPy on github https://github.com/ZimboPy

    38분

  3. 2021. 09. 08.

    Even Oldridge on Tabular Deep Learning and the Future of Recommender Systems

    This week we’re joined by Even Oldridge, Senior Manager, RecSys Platform Team at NVIDIA. We talk about Tabular Deep Learning, NVMerlin, how bookstores aren’t like recommender systems, his team’s recent repeat win in the ACM Recsys Challenge, the future of recommender systems and more. NVIDIA Merlin on the NVIDIA Developer Blog https://developer.nvidia.com/blog/tag/merlin/ NVIDIA Merlin blogs on Medium https://medium.com/nvidia-merlin Merlin on Github https://github.com/NVIDIA-Merlin/Merlin NVTabular Blogs https://developer.nvidia.com/blog/tag/nvtabular/ NVTabular on Github https://github.com/NVIDIA/NVTabular REES46 data set mentioned toward the end of the podcast https://rees46.com/en/datasets

    42분

  4. 2021. 08. 26.

    Way of the Grandmaster 2 with Christof Henkel

    We talk with 4-time Kaggle winner Christof Henkel about how he got started in Kaggle, important skills for Kaggle success, his most memorable contests, his most recent victory, how an alien radio signal is like a bird call, climbing at the 2021 Olympics, and much more!  Christof's Kaggle Profile: https://www.kaggle.com/christofhenkel Christof's Twitter: https://twitter.com/kagglingdieter

    38분

  5. 2021. 08. 05.

    Way of the Grandmaster with Chris Deotte

    We sit down and talk with 4x Kaggle Grandmaster Chris Deotte about his career, how he got started doing Kaggle, how you can get started doing Kaggle, feature engineering, the perks of AGI, and a lot more!  Chris on Kaggle: https://www.kaggle.com/cdeotte

    46분

  6. 2021. 07. 08.

    Data Science, Social Science, and the Near Future of RAPIDS with John Zedlewski

    I sit down and talk with the new Director of Engineering for RAPIDS at NVIDIA, John Zedlewski about what economics can learn from machine learning practitioners, engineering challenges that ended up being harder than first thought, how increased automation will change the day-to-day work of data scientists, and much more.

    34분

  7. 2021. 06. 25.

    Simulating large-scale numerical models in natural science with Zahra Ronaghi and Christoph Keller

    I talk with Zahra Ronaghi, Engineering Manager of AI Infrastructure at NVIDIA and Christoph Keller, Atmospheric Chemist with the NASA Goddard Space Flight Center about their collaboration to bring GPU-accelerated data science to the study of air pollution. You can find their first blog on the collaboration here and their work around the atmospheric impact of COVID here. For more about GPU accelerated shape this blog is a good place to start.

    38분

  8. 2021. 06. 10.

    Community, Whisky, Fitness, and Data Science with Jim Scott

    On this week’s episode, we have NVIDIA’s Head of Developer Relations, Data Science, Jim Scott. We talk about the data science of fine whiskey, data science for fitness, the “secret” of Kaggle Grand Masters (spoiler: it’s giving back to the community), learning and community resources as the future of data science, classic “paradoxes” in basic probability, and some great resources for being a better data scientist. Kaggle Grandmaster Youtube Interviews - Here’s the most recent sit down Jim did with the Kaggle Grand Masters of NVIDIA. https://www.youtube.com/watch?v=bHuww-l_Sq0 Data Science of the Day - we talk about this toward the end of the episode, and this is a GREAT resource to keep up-to-date with everything going on in data science. https://forums.developer.nvidia.com/c/ai-data-science/data-science-of-the-day/323/none Jim on Twitter: https://twitter.com/kingmesal Jim and I reminisce about the Birthday Paradox - here’s a good piece on it from Scientific American. Jim and I were way off on remembering how likely birthday sharing is in a small handful of people. https://www.scientificamerican.com/article/bring-science-home-probability-birthday-paradox/ Don’t let us get your goat talking about the Monty Hall Problem. This explainer shows how an example with a larger number of doors can help give more intuition about what’s actually happening by changing your guess. https://www.statisticshowto.com/probability-and-statistics/monty-hall-problem/ Cantor’s Diagonalization Theorem mentioned in passing. Here’s a link to the wikipedia article - if you aren’t familiar with it, you should check it out. https://en.wikipedia.org/wiki/Cantor%27s_diagonal_argument

    31분
모두 보기(15개)

평가 및 리뷰

4.9
최고 5점
13개의 평가

소개

Follow the RAPIDSFire podcast for a fresh take on data science. Hear from revolutionaries transforming data science on GPUs for scientific research, higher education, and the broader enterprise. Talks with open-source software maintainers, Kaggle grandmasters, practitioners, CUDA experts and many others keep you up-to-date on the most exciting developments. Let's discuss how to make your work better and faster. Hosted by Data Scientist Paul Mahler. Join the conversation and send feedback on Twitter @rapidsai

정보

  • 제작진
    RAPIDS
  • 방송 연도
    2020년 - 2021년
  • 에피소드
    15
  • 등급
    전체 연령 사용가
  • 저작권
    © RAPIDS
  • 웹사이트 보기
    RAPIDSFire