Guess – In machine learning math there is a connection between complex numbers and variation. So in F.sum(X) numbers. They to have variation in X. So there is a possibility for more variation in matrix multiplication other than F.sum() // Per

Guess – In machine learning math there is a connection between complex numbers and variation. So in F.sum(X) numbers. They to have variation in X. So there is a possibility for more variation in matrix multiplication other than F.sum() // Per

Machine Learning Innovation – Since frequencies don’t change. They can then be used as universal layer loss function values. So each layer is not allowed to change the frequency. You use that as a limiter for each layer. Everything in the universe is enabling larger models // Per

Machine Learning Innovation – Since frequencies don’t change. They can then be used as universal layer loss function values. So each layer is not allowed to change the frequency. You use that as a limiter for each layer. Everything in the universe is enabling larger models // Per

Machine Learning Battery Innovation Food-Mimicry – Does there exist something like food-mimicry for getting inspiration for batteries. Are there general problems that would be the same. Both are heated. Do spices have other functions in batteries. // Per

Machine Learning Battery Innovation Food-Mimicry – Does there exist something like food-mimicry for getting inspiration for batteries. Are there general problems that would be the same. Both are heated. Do spices have other functions in batteries. // Per

Machine Learning Electric Plane Innovation – Use machine learning model() to mitigate beam bending of the wings. To know when to use power that is. Every reltative position and event in the plane body must coincide with the motors. // Per

Machine Learning Electric Plane Innovation – Use machine learning model() to mitigate beam bending of the wings. To know when to use power that is. Every reltative position and event in the plane body must coincide with the motors. // Per