October 2021
Intermediate to advanced
344 pages
8h 51m
English
Content preview from Math for Deep Learning
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3MORE PROBABILITY
Chapter 2 introduced us to basic concepts of probability. In this chapter, we’ll continue our exploration of probability by focusing on two essential topics often encountered in deep learning and machine learning: probability distributions and how to sample from them, and Bayes’ theorem. Bayes’ theorem is one of the most important concepts in probability theory, and it has produced a paradigm shift in the way many researchers think about probability and how to apply it.
Probability Distributions
A probability distribution can be thought of as a function that generates values on demand. The values generated are random—we don’t ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.