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Cake day: July 1st, 2024

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  • Funny thing there is actually attempts at modeling uncertainty in Deep Learning. But they are rarely used because they are either super inaccurate or have super slow convergence. (MCMC, Bayesian neural networks) The problem is essentially that learning algorithms cannot properly integrate over certainty distributions, so only an approximation can be trained, which is often pretty slow.

















  • bratorange@feddit.orgtoScience Memes@mander.xyzDo you know the answer?
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    2 months ago

    This can also be used a great example of proof by contradiction: There is no correct answer in the options. Proof: Assume there was a correct answer in the options. Then it must be either 25%, 50% or 60%. Now we make a case distinction.

    (A) Assume it was 25. Then there would be two of four correct options yielding in a probability of 50%. Therefore 50 must be the correct answer. -> contradiction.

    (B) Assume it was 50. Then there would be one of four correct options yielding in a probability of 25%. Therefore the answer is 25. -> contradiction.

    © Assume it was 60%. Since only 0,1,2,3 or 4 of the answers can be correct the probability of choosing the right answer must be one of 0% 25% 50% 75% or 100%. -> contradiction.

    Because of (A), (B) and ©, it cannot be 25, 50% or 60%. -> contradiction.





  • bratorange@feddit.orgtoScience Memes@mander.xyzLiquid Trees
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    2 months ago

    Like I always think that people don’t get one thing about trees in a city. There purpose is is not about co2. The co2 reduction of city trees is neglectable. The reason you need them in a city is temperature regulation, shade, air quality, mood, the local eco system and maybe solidifying unsealed ground. Putting these tanks in a city is laughably inefficient w.r.t. co2 conversion if you compare this to any effort to do this in instustrial capacity ( which is is also still laughably inefficient)