Practical Machine Learning

Algorithms and Concurrency

Let's now look at some basics of algorithms in general, the time complexity; and the order of magnitude measurements, before we start talking about building concurrency in executing algorithms, then explore the approaches to parallelizing algorithms.

An algorithm can be defined as a sequence of steps that takes an input to produce the desired output. They are agnostic technology representations; let's look at a sorting algorithm example:

Input: A sequence of n number—a1, a2, …,an
Output: A permutation (reordering)—a1', a2', …,an' such that a1'<=a2'<=… <=an'

The following algorithm is an insertion-sort algorithm:

INSERTION-SORT(A) 1. for j = 2 to length[A] 2. dokey<-A[j] 3. //insert A[j] to sorted sequence A[1..j-1] 4. i<-j-1 ...