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Probability - Math for Machine Learning
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In this video, W&B's Deep Learning Educator Charles Frye covers the core ideas from probability that you need in order to do machine learning.
In particular, we'll see why mathematically rigorous probability theory is so challenging, and then go over why negative logarithms of probabilities, aka "surprises", show up so often in machine learning.
0:00 Introduction
1:45 Probability is subtle
7:45 Overview of takeaways
8:47 Probability is like mass
17:51 Surprises show up more often in ML
21:46 Surprises give rise to loss functions
24:31 Surprises are better than densities
33:35 Gaussians unite probability and linear algebra
39:57 Summary of the Math4ML ideas
41:40 Additional resources on Math4ML
In particular, we'll see why mathematically rigorous probability theory is so challenging, and then go over why negative logarithms of probabilities, aka "surprises", show up so often in machine learning.
0:00 Introduction
1:45 Probability is subtle
7:45 Overview of takeaways
8:47 Probability is like mass
17:51 Surprises show up more often in ML
21:46 Surprises give rise to loss functions
24:31 Surprises are better than densities
33:35 Gaussians unite probability and linear algebra
39:57 Summary of the Math4ML ideas
41:40 Additional resources on Math4ML
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