I am glad you made it here. And since you are reading this, I expect that you are quite well familiar with the terms like ‘Neural Networks’, ‘Backpropagation’, ‘Overfitting’, ‘Underfitting’, ‘Learning’, ‘Gradient Checking’, and ‘Loss functions’ etc.
If you didn’t, no worries, I will save them for next stories. But here, we can start with Optimization.
Optimization is a process of finding quality parameters for our neural network “Remember by parameters we mean the weights and biases”
One of the main challenges in implementation of Kruskal’s algorithm is deciding whether to add edge (u, v) to a component or not. What we need to do there is, we need to find the components that u and v lie in, and see if they are the same or not. If they’re the same, don’t add, if they’re different, do add.
We need a data structure that support operations of union and find.
So the union-find implementation works as follows, we maintain all the vertices, all the connected components, and the members that the components are gonna live in…
We know that Quicksort, Mergesort and Heapsort are three top sorting algorithms use to sort an array. They all have a time complexity of O(nlogn).
While in mergesort, the problem was in its ground details. It actually takes extra space in the memory during merge operation.
Randomization is a technique which has been frequently used to handle the worst case inputs. The idea behind is to randomize our choices so that as the program proceeds none of the input remains worst case.
We hope that we get good luck with randomization and that it happens with high probability.
It was my first attempt to do this job which actually intended json responses of python programs for my web app. I was even fortunate to be an AWS educate.
To see the procedure from start you may see:
His content is accurate and sufficient. However I will like to discuss some of the problems I had to face with a hope that it might help you.
Well firstly I did this task on Windows Machine so had to use putty.
Secondly my server wasn’t the same as used in example. It was a ubuntu-focal-20.04-amd64-server-20201026. …