5 февр. 2021 г. · So normal distribution is useful and provides theoretical basis for doing population level parameter estimates from samples (think of election ...

9 июн. 2023 г. · Overall, the use of normal distributions in deep learning enables effective initialization, efficient optimization, noise modeling, uncertainty ...

5 дек. 2020 г. · The "normal distributions work better for machine learning" typically refer to feature values, not target values.

5 февр. 2021 г. · Why is it important to know if my feature is following a Normal Distribution ? -> My guess : some models need it for faster convergence or ...

16 дек. 2019 г. · Cases where the model will actually perform better with a normally distributed target include, among others, Gaussian process regression, ...

13 авг. 2022 г. · But I often see people make the data even more "normal" by transforming the data so its shape better matches the normal distribution, not just ...

9 июл. 2019 г. · In the context of deep learning, normalization usually refers to the process of subtracting the mean and dividing by the standard deviation.

22 мар. 2022 г. · The short answer is no, you don't always need to transform your data to a normal distribution. This depends a lot on the learning algorithm you're using.

25 мая 2020 г. · Transforming your target via a logarithmic function linearizes your target. Which is useful for many models which expect linear targets.

8 июн. 2020 г. · Wikipedia says that features do not need to be normally distributed, but it has a major influence on the precision of estimates.