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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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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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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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