4 ms·
The line between approximate and compute is a blurry one: some may say if the polynomial approximation depends on log of the error, meaning you can exponentiall
by bhl 7y ago
The line between approximate and compute is a blurry one: some may say if the polynomial approximation depends on log of the error, meaning you can exponentially get better with each step, computation and approximation are the same.
- sl1ck731 7y agoI think it is important in the context of what people are trying to use NNs for. Most of the time it is some type of classification, in which case there is always a chance it will be completely wrong in the sense that anything other than the expected output is not correct in any way. Which leads into unknowingly building NNs that are actually building classification networks and not realizing approximations might not fit into your model.
- pauljurczak 7y agoMy elementary school teacher would disagree: 2+2=4 not somewhere around 3.997.
- deleted 7y ago[deleted]