terminology

I'm reading "Introduction to Algorithm" by CLRS. In chapter 2, the authors mention "loop invariants". What is a loop invariant? Tomas Petricek In simple words, a loop invariant is some predicate (condition) that holds for every iteration of the loop. For example, let's look at a simple for loop that looks like this: int j = 9; for(int i=0; i<10; i++) j--; In this example it is true (for every iteration) that i + j == 9 . A weaker invariant that is also true is that i >= 0 && i <= 10 . TNi I like this very simple definition: ( source ) A loop invariant is a condition [among program variables]

What is the difference between epoch and iteration when training a multi-layer perceptron? In the neural network terminology: one epoch = one forward pass and one backward pass of all the training examples batch size = the number of training examples in one forward/backward pass. The higher the batch size, the more memory space you'll need. number of iterations = number of passes, each pass using [batch size] number of examples. To be clear, one pass = one forward pass + one backward pass (we do not count the forward pass and backward pass as two different passes). Example: if you have 1000

I understand the concept of database normalization, but always have a hard time explaining it in plain English - especially for a job interview. I have read the wikipedia post, but still find it hard to explain the concept to non-developers. "Design a database in a way not to get duplicated data" is the first thing that comes to mind. Does anyone has a nice way to explain the concept of database normalization in plain English? And what are some nice examples to show the differences between first, second and third normal forms? Say you go to a job interview and the person asks: Explain the