Ciro Santilli @cirosantilli 37

The table: en.wikipedia.org/w/index.php?title=MNIST_database&oldid=1152541822#Classifiers

Getting started tutorial: unix.stackexchange.com/questions/223078/best-way-to-grep-a-big-binary-file/758528#758528

An elliptic curve is defined by numbers $a$ and $b$. The curve is the set of all points $(x,y)$ of the real plane that satisfy the Equation 1. "Definition of the elliptic curves"

Equation 1. "Definition of the elliptic curves" definies elliptic curves over any field, it doesn't have to the real numbers. Notably, the definition also works for finite fields, leading to elliptic curve over a finite fields, which are the ones used in Elliptic-curve Diffie-Hellman cyprotgraphy.

It is true, something Ciro Santilli often things about. One likely reason is that the world is broken and most cyclist are speed maniacs willing to put the time in. Unlike Dutch people where everyone cycles.

The elliptic curve group of an elliptic curve is a group in which the elements of the group are points on an elliptic curve.

The group operation is called elliptic curve point addition.

Bibliography:

This is a family of notations related to the big O notation. A good mnemonic summary of all notations would be:

Analogous to a linear form, a bilinear form is a Bilinear map where the image is the underlying field of the vector space, e.g. ${\mathbb{R}}^n\times {\mathbb{R}}^m\to \mathbb{R}$.

Some definitions require both of the input spaces to be the same, e.g. ${\mathbb{R}}^n\times {\mathbb{R}}^n\to \mathbb{R}$, but it doesn't make much different in general.

The most important example of a bilinear form is the dot product. It is only defined if both the input spaces are the same.

The Equation "Definition of the elliptic curves" and definitions on elliptic curve point addition both hold directly.

This is likely a joke binet, but the idea is epic: its members would in principle take the hardest courses and purposefully get bad grades on them to improve the grades of others, as grades are always normalized to a normal distribution.

Mnemonic: sur means over. So we are going over the codomain, and covering it entirely.