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1

The energy usage will vary depending on the machine, but as long as all of your results are from the same setup there's a comparatively low-tech, simple solution: Hook up your computer to an electricity meter and take some measurements with the computer "at rest", to get a baseline of its energy consumption. Then, run several trials with each model you're ...


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Purely randomness is not possible but in real-world the most randomness that a neural network can process is enough to discover an approximation pattern; the precision depends on the model of the context (exactitude of the Digital Twins) and processing power. To extract the exact pattern from the pure randomness (I think) is impossible even with a quantum ...


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This is maybe not the answer you are looking for but I hope it will clear some things up. Humans make models to better understand things and to predict behaviors. Do atoms (electrons, positrons, neutrons) actually exist ? Have we seen them ? Is it able to see them ? The answer is no. BUT, the atom theory explains perfectly all the experiments that have been ...


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(Not enough reputation to comment, so writing here.) Unless your algorithms are secret, please post your algorithms here. Maybe someone (not me though) can find a library for you. Maybe someone can tell how long does it take to implement it. Use Git and GitHub. You can rollback bad code with this. Always write tests. This helps against regressions as you ...


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I have been implementing a branch and bound solver with heuristics for an NP-hard problem. It got complicated at some points and had to reimplement parts a couple of times. The problem was (I think), that I started implementing with only an intuition about the design and how it looks like. That is bad software engineering and is catastrophic in big project. ...


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some says it is because of this architecture SVM is comparable to neural nets


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We apply one filter to 32x32x3 image and output 32x32x1 image.And we stack all these twelve 32X32X1 images,which gives 32x32x12 volume. check this image.In here we apply 5x5x3 kernal to 32x32x3 image with zero padding and stride 1.which gives you 28x28x1 output.if we use 12 filters like this,we get 28x28x12 volume.


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