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Data Science at Home

Data Science at Home

著者: Francesco Gadaleta
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Cutting through AI bullsh*t.
Come join the discussion on Discord!
https://discord.gg/4UNKGf3

Copyright 2026 datascienceathome.com All rights reserved.
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  • Your AI Strategy is Burning Money: Here's How to Fix It (Ep.295)
    2025/11/25

    Most companies don't have an AI problem. They have a decision-making problem. Matt Lea, founder of Schematical and CloudWarGames, has spent nearly 20 years helping tech leaders ship smarter.

    In this conversation, he breaks down when AI actually makes sense, where AWS costs spiral out of control, and why your "cool demo" keeps dying before launch. If you're tired of AI hype and ready for straight answers, hit play. Join the conversation! Our Discord community is full of ML engineers, researchers, and AI enthusiasts discussing papers, sharing projects, and helping each other level up. Whether you're debugging your first neural net or training your tenth transformer, there's a place for you.

    Newsletter https://datascienceathome.substack.com/subscribe

    Website https://datascienceathome.com

    References

    http://schematical.com

    https://cloudwargames.com

    https://schematical.com/posts/we-need-ai_20241028

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    32 分
  • From Tokens to Vectors: The Efficiency Hack That Could Save AI (Ep. 294)
    2025/11/11

    LLMs generate text painfully slow, one low-info token at a time. Researchers just figured out how to compress 4 tokens into smart vectors & cut costs by 44%—with full code & proofs! Meanwhile OpenAI drops product ads, not papers. We explore CALM & why open science matters. 🔥📊

    Sponsors

    This episode is brought to you by Statistical Horizons At Statistical Horizons, you can stay ahead with expert-led livestream seminars that make data analytics and AI methods practical and accessible. Join thousands of researchers and professionals who’ve advanced their careers with Statistical Horizons. Get $200 off any seminar with code DATA25 at https://statisticalhorizons.com

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    47 分
  • Why AI Researchers Are Suddenly Obsessed With Whirlpools (Ep. 293)
    2025/10/30

    VortexNet uses actual whirlpools to build neural networks. Seriously. By borrowing equations from fluid dynamics, this new architecture might solve deep learning's toughest problems—from vanishing gradients to long-range dependencies. Today we explain how vortex shedding, the Strouhal number, and turbulent flows might change everything in AI.

    Sponsors

    This episode is brought to you by Statistical Horizons At Statistical Horizons, you can stay ahead with expert-led livestream seminars that make data analytics and AI methods practical and accessible. Join thousands of researchers and professionals who’ve advanced their careers with Statistical Horizons. Get $200 off any seminar with code DATA25 at https://statisticalhorizons.com

    References

    https://samim.io/p/2025-01-18-vortextnet/

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    33 分
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