The question1: what the normed linear space(赋范线性空间) is?
For sloving the question, I will divide it into two parts, which are norm and linear space!
what the distance is?There are three kinds of distance.
condition 1: Euclidean distance and that is 
condition 2 :
condition 3: 
which is called manhattan distance
And no matter which kind distance, they all satisfy the three rules above.
We can add another rule so that we can get a more concrete idea that we call norm.
There are also three cases of norm corresponding to distance:
what is linear space?
The vector spaces which satisfiy the eight rules are linear space!
A linear space that has a norm is called an normed linear space.
Question 2:The definition of a graph in computer science.
In the processing of finishing, I found a very good blog, which speaks very comprenhensive and I share it here:
https://blog.csdn.net/qq_35644234/article/details/57083107
Question 3:someting about activation functions
https://www.jiqizhixin.com/articles/2017-11-02-26
I can always find good articles which can solve my questions!
Question 4:compact supset of 
https://blog.csdn.net/foolely/article/details/1350785 LOL
Question5：Why can neural networks approach arbitrary continuous functions infinitely?
https://zhuanlan.zhihu.com/p/25590725
This is a article on www.zhihu.com and I think neural networks can solve nonlinear functions by increasing the numbers of neural cells and adjusting parameters.
Question6:Calculate the relationship between output y and input x in the condition of only one hidden layers
This slide is from Andrew Ng's courese called deep learing on coursehero. And it clearly shows the process of calculating.