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The post you linked to clearly states that pseudo random number cannot be predicted. Their randomness is made to be nearly perfect, and if you ever found a way to even predict a pseudo random number with 20% chance of correct, the security of the entire world would be vulnerable to attacks, as things ranges from cryptocurrency and secure data transfer is all ...


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Why AI is (or not) a good option for the generation of random numbers? AI approaches are generally not good for generating random numbers, for these reasons: Similar to why they are not good for adding numbers, there already exist many strong pseudo-random and "true" random sources, possible without using any AI approach, and demonstrably good enough for ...


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I don't think you can. Say a NN with 3 layers gives an accuracy of 95.3% and another NN with 4 layers gives an accuracy of 95.4%. Then there is no guarantee that the 4 layer NN is better than the 3 layered NN. Since with different initial values the 3 layer NN might perform better. You could run multiple times and probabilistically say that this is better, ...


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AI is supposed to do anything human or traditional computer can do, that is what we expect AI to be. Technically you would need AGI (Artifical General Intelligence) to do anything a human can do. This is not a technology that exists, but a goal of some AI research to perform more and more general tasks. So 'generating random value' is also a task ...


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There are other sources that will lead to different results in addition to weight initialization. For example dropout layers. Make sure you specify the random seed.Also data reading using flow from directory,make sure you set shuffle to False or if you do not then set the random seed. If you use transfer learning make that part of your network non-trainable. ...


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There are two weight-initializing methods for neural networks: 1-Zero initializing 2-Random initializing https://towardsdatascience.com/weight-initialization-techniques-in-neural-networks-26c649eb3b78 If you choose zero initalizing method in every train loop, you may get same results OR you can use transfer learning according to your problem, it allows ...


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If is a truly a random number, and you could guess each of the next successive five in sequence, then you could win the lottery consistently. This is one of the first tasks many people try to do when first learning machine learning. If the lottery is truly a random physical process with fair, i.e., balanced ping pong balls, then you cannot predict which ...


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Let me add an example from machine learning that shows that resorting to randomness is the optimal way, sometimes. When working on the whole data is not tractable (computation cost, data does not fit in memory), working on random samples can be an optimal way to train a machine learning algorithm. One of the most used optimization technique in those cases ...


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