Big(O) Notation Is About Scalability, Not Speed
When comparing algorithms based on execution time, computer scientists use Big(O) notation. Big(O) describes how an algorithm's running time grows as the size of the input grows.
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When comparing algorithms based on execution time, computer scientists use Big(O) notation. Big(O) describes how an algorithm's running time grows as the size of the input grows.
We live in a three-dimensional world, so most of the mathematics we learn is limited to 3D. However, the same mathematical ideas can be extended to any number of dimensions.
Machine learning models generally learn from data in one of two ways: Direct (Analytical) Learning or Iterative Optimization. Understanding these two approaches provides a useful big-picture view of how many machine learning algorithms are related.
At a high level, supervised learning focuses on recognizing previously seen information, while unsupervised learning focuses on discovering previously unseen information.
This blog is about three ways computers solve problems: by following rules, by learning patterns from data, and by generating answers that may not have a clear right or wrong.
There are two broad types of algorithms used in software: conventional algorithms and AI algorithms. This section gives a brief comparison between the newer, increasingly popular AI-based algorithms and the older, conventional approach.