10 Probability Concepts for Machine Learning Explained Simply
# Introduction to Probability Concepts For a long time I treated probability as the vegetables of machine learning. The boring stuff you choke down before you get to the good part. Later on, I realized that probability is not just a prerequisite for machine learning but it makes much of...
# Introduction to Probability Concepts For a long time I treated probability as the vegetables of machine learning. The boring stuff you choke down before you get to the good part. Later on, I realized that probability is not just a prerequisite for machine learning but it makes much of machine learning work. But nobody tells you up front that a model is almost never sure of anything. Is this image a cat? Probably. Is this transaction fraud, or did someone just buy a lot of socks at 2am? Hard to say. A language model has no clue what word you're about to type next, so it doesn't pretend to. It spreads its confidence across a bunch of options and hands you the most likely one. Once that clicked for me, a lot of the rest stopped feeling like memorization. You don't need a stats PhD for any of this. You need maybe ten ideas. Here they are, the way I wish someone had explained them to me: