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  • بودكاست كلام في البرمجة
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    بودكاست كلام في البرمجة

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    GoogleTHAT

    Pouge

  • Certified: The CompTIA DataAI Audio Course
    Certified: The CompTIA DataAI Audio Course

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    Certified: The CompTIA DataAI Audio Course

    Jason Edwards

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  • Intelligence Artificielle - Data Driven 101 - Le podcast IA & Data 100% en français
    Intelligence Artificielle - Data Driven 101 - Le podcast IA & Data 100% en français

    5

    Intelligence Artificielle - Data Driven 101 - Le podcast IA & Data 100% en français

    Marc Sanselme - Draft'n run - Studio IA no-code

  • The Art of Space Engineering
    The Art of Space Engineering

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    The Art of Space Engineering

    Sarah Rogers

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    Intelligent Transportation Systems Podcast

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    Intelligent Transportation Systems Podcast

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  • برق مع عبدالله السبع
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    Updated 15/10/2020

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  • ازاي انجح في مجال علوم البيانات (بترول المستقبل) - عمرو صالح

    21/09/2024

    1

    ازاي انجح في مجال علوم البيانات (بترول المستقبل) - عمرو صالح

    ضيف الحلقة هو المهندس / عمرو صالح .. خبرة اكتر من 12 سنة في تخصص علوم البيانات Data Science - Data Engineering. جاوبنا على كل الأسئلة المتعلقة بتخصصات ووظائف علوم البيانات .. وهو سافر نيوزلندا واشتغل مع اكبر شركة اتصالات هناك وصمم الانظمة المتعلقة بتحليل البيانات واستخراج المعلومات وانشاء التقارير. عنده شركته الخاصة Kiwilytics اللي بتقدم خدمات الأنظمة اللي بتخدم التخصص ده .. وأيضاً بيوفر فرص للشباب المتحمس اللي عايز يدخل التخصص ده يتدرب عنده في الشركة يبدأوا من الصفر وممكن ياخدوا الفرصة انهم يشتغلوا على انظمة تعمل في شركات على مستوى العالم. وهو برده بيقدم Roadmap لاي حد حابب يدخل التخصص .. وهو شخص مرح وبشوش وانا سعدت جداً باستضافته وصديق ان شاء الله لسنين قادمة. شوفوا الحلقة ومتنسوش تعملوا شير مع صحابكم ✌️🚀

    21/09/2024

    •
    1hr 56min
  • #1 - Ultraviolet Optical Systems with Dr. Paul Scowen

    02/01/2021

    2

    #1 - Ultraviolet Optical Systems with Dr. Paul Scowen

    Studying stars and planetary objects in the ultraviolet can reveal a tremendous amount about the way our universe works. However, developing the instruments to study them is not a trivial thing to do. In this episode, I chat with Dr. Paul Scowen about the design of UV optical systems and how these are also driven by spacecraft interfaces, how these instruments are tested and calibrated, and challenges that are faced in collecting data in the UV. Dr. Scowen is a research professor at Arizona State University’s School of Earth and Space Exploration, where his work is centered around star and planet formation in massive stellar environments as well as optical engineering for instruments operating in the UV spectrum. Additional resources: HabEx concept paper: https://www.jpl.nasa.gov/habex/pdf/HabEx-Final-Report-Public-Release-LINKED-0924.pdf SPARCS: https://sese.asu.edu/research/sparcs

    02/01/2021

    •
    1hr 18min
  • Uncertainty Quantification for Neural Networks with Pytorch Lightning UQ Box

    24/05/2024

    3

    Uncertainty Quantification for Neural Networks with Pytorch Lightning UQ Box

    In this episode, I caught up with Nils Lehmann to learn about Uncertainty Quantification for Neural Networks. The conversation begins with a discussion on Bayesian neural networks and their ability to quantify the uncertainty of their predictions. Unlike regular deterministic neural networks, Bayesian neural networks offer a more principled method for providing predictions with a measure of confidence. Nils then introduces the Pytorch Lightning UQ Box project on GitHub, a tool that enables experimentation with a variety of Uncertainty Quantification (UQ) techniques for neural networks. Model interpretability is a crucial topic, essential for providing transparency to end users of machine learning models. The video of this conversation is also available on YouTube here * Nils’s website * Lightning UQ box on Github * Further reading: A survey of uncertainty in deep neural networks Bio: Nils Lehmann is a PhD Student at the Technical University of Munich (TUM), supervised by Jonathan Bamber and Xiaoxiang Zhu, working on uncertainty quantification for sea-level rise. More broadly his interests lie in Bayesian Deep Learning, uncertainty quantification and generative modelling for Earth Observational data. He is also passionate about open-source software contributions and a maintainer of the Torchgeo package. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.satellite-image-deep-learning.com

    24/05/2024

    •
    26 min
  • TikTok

    08/04/2021

    4

    TikTok

    Tiktok: qué es y nuestra opinión

    08/04/2021

    •
    9 min
  • The Android Show Recap!

    15 May

    5

    The Android Show Recap!

    The Android Show was this week and gave us a bunch of things to talk about! With Andrew and David out this week, Marques, Adam, and Mariah break down everything that Google showed off. Then Ellis and Rufus came up with a game for everyone to play by combining Family Feud and...Reddit? Then they wrap it all up talking about the new Fitbit Air! Links: Google - Android Show I/O Edition Google - Fitbit Air Follow us on socials: Marques: ⁠⁠https://www.threads.net/@mkbhd⁠⁠ Andrew: ⁠⁠https://www.threads.net/@andrew_manganelli⁠⁠ David: ⁠⁠https://www.threads.net/@davidimel⁠⁠ Adam: ⁠⁠https://www.threads.net/@parmesanpapi17⁠⁠ Ellis: ⁠⁠https://twitter.com/EllisRovin⁠⁠ Mariah: ⁠⁠https://www.instagram.com/totallynotabusinessacc/⁠⁠ Rufus: https://www.instagram.com/rmullhaupt/ Waveform Threads: ⁠⁠https://www.threads.net/@waveformpodcast⁠⁠ Waveform Instagram: ⁠⁠https://www.instagram.com/waveformpodcast/?hl=en⁠⁠ Waveform TikTok: ⁠⁠https://www.tiktok.com/@waveformpodcast⁠⁠ Join the Discord: ⁠⁠https://discord.gg/mkbhd⁠⁠ Intro/Outro music by 20syl: ⁠⁠https://bit.ly/2S53xlC⁠⁠ Waveform is part of the Vox Media Podcast Network. Learn more about your ad choices. Visit podcastchoices.com/adchoices

    15 May

    •
    1hr 39min
  • Engineering Spacecraft Components at Scale for Commercial Space Growth

    5 days ago

    6

    Engineering Spacecraft Components at Scale for Commercial Space Growth

    On this episode of the Aerospace & Defense Technology podcast, Brian Ippolitto, Vice President of Space Systems, discusses how spacecraft component engineering is evolving to meet the accelerating demands of commercial space growth. Drawing on more than a decade of experience across engineering, operations, and business development, he outlines the transition from low-volume development programs to high-rate manufacturing — now delivering tens of thousands of precision components annually for satellites, launch vehicles, and deep-space missions. The conversation explores how propulsion systems and critical subsystems are being scaled to production, how quality and reliability are maintained at volume, and what it takes to support the next phase of commercial space expansion. Sponsored by: Omnetics and New England Wire

    5 days ago

    •
    13 min
  • 1003: Building an AI Data Center End to End, with Lightning AI’s Frank Basso

    1 day ago

    7

    1003: Building an AI Data Center End to End, with Lightning AI’s Frank Basso

    Frank Basso, VP of Infrastructure at Lightning AI, joins Jon Krohn for a rare ground-level tour of the one layer of the AI stack the show had never covered in over a thousand episodes: the physical data center. Frank explains how Lightning AI provisions its 35,000-plus GPUs through hyperscale co-location, why everything new is liquid-to-chip cooled, how GPUs talk to each other over ultra-fast east-west networks, and what it’s actually like to stand inside a 110-decibel AI data hall. He also debunks the most persistent myths about data-center water and electricity use, and makes the case for fuel cells, nuclear power, and 800-volt DC distribution as the path forward. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.superdatascience.com/1003⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information. In this episode you will learn: (02:47) What actually makes an AI data center different from a traditional one (06:04) How Lightning AI provisions its 35,000+ GPUs through hyperscale co-location (24:01) Why liquid cooling doesn’t waste water, debunking the biggest data-center myth (29:46) East-west vs. north-south networks, explained (43:47) “Screaming banshees”: why AI data halls run at 105–110 decibels (51:52) Why data centers don’t actually drive up your power bill

    1 day ago

    •
    1hr 12min
  • Episode 33 — Understand loss functions and why optimization targets behavior

    Episode 33

    8

    Episode 33 — Understand loss functions and why optimization targets behavior

    This episode teaches loss functions as the contract between your objective and your model’s behavior, which is a frequent DY0-001 theme when questions ask why a model “acts” a certain way. You’ll define loss as a numeric penalty for being wrong, then connect common losses to what they emphasize, such as squared error’s sensitivity to outliers, absolute error’s robustness, and cross-entropy’s focus on probabilistic separation in classification. We’ll explain why the choice of loss shapes gradients, training stability, and the kinds of errors a model tolerates, and we’ll tie that to real-world scenarios like fraud detection, forecasting, and safety screening. Best practices will include aligning loss to evaluation metrics, using weighted losses for imbalance, and avoiding the trap of optimizing one thing and reporting another. Troubleshooting covers unstable training due to mismatched loss and activation, poor calibration caused by the wrong objective, and apparent “accuracy” gains that hide costly failure modes. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your educational path. Also, if you want to stay up to date with the latest news, visit DailyCyber.News for a newsletter you can use, and a daily podcast you can commute with.

    E33

    •
    16 min
  • Navigating Common Pitfalls in Data Science: Lessons from Pierpaolo Hipolito - ML 183

    24/01/2025

    9

    Navigating Common Pitfalls in Data Science: Lessons from Pierpaolo Hipolito - ML 183

    Welcome to another insightful episode of Top End Devs, where we delve into the fascinating world of machine learning and data science. In this episode, host Charles Max Wood is joined by special guest Pierpaolo Hipolito, a data scientist at the SAS Institute in the UK. Together, they explore the intriguing paradoxes of data science, discussing how these paradoxes can impact the accuracy of machine learning models and providing insights on how to mitigate them. Pierpaolo shares his expertise on causal reasoning in machine learning, drawing from his master's research and contributions to Towards Data Science and other notable publications. He elaborates on the complexities of data modeling during the early stages of the COVID-19 pandemic, highlighting the use of simulation and synthetic data to address data sparsity. Throughout the conversation, the focus remains on the importance of understanding the underlying system being modeled, the role of feature engineering, and strategies for avoiding common pitfalls in data science. Whether you are a seasoned data scientist or just starting out, this episode offers valuable perspectives on enhancing the reliability and interpretability of your machine learning models. Tune in for a deep dive into the paradoxes of data science, practical advice on feature interaction, and the importance of accurate data representation in achieving meaningful insights. Become a supporter of this podcast: https://www.spreaker.com/podcast/adventures-in-machine-learning--6102041/support.

    24/01/2025

    •
    55 min

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