Shortform link:
In this video we will talk about backpropagation – an algorithm powering the entire field of machine learning and try to derive it from first principles.
OUTLINE:
00:00 Introduction
01:28 Historical background
02:50 Curve Fitting problem
06:26 Random vs guided adjustments
09:43 Derivatives
14:34 Gradient Descent
16:23 Higher dimensions
21:36 Chain Rule Intuition
27:01 Computational Graph and Autodiff
36:24 Summary
38:16 Shortform
39:20 Outro
USEFUL RESOURCES:
Andrej Karpathy’s playlist:
Jürgen Schmidhuber’s blog on the history of backprop:
~juergen/
CREDITS:
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