Introduction

Historical background

Curve Fitting problem

Random vs guided adjustments

Derivatives

Gradient Descent

Higher dimensions

Chain Rule Intuition

Computational Graph and Autodiff

Summary

Shortform

Outro

Artem Kirsanov

Shortform link: 
https://shortform.com/artem

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: https://youtube.com/playlist?list=PLAqhIrjkxbuWI23v9cThsA9GvCAUhRvKZ&si=zBUZW5kufVPLVy9E

Jürgen Schmidhuber's blog on the history of backprop:
https://people.idsia.ch/~juergen/who-invented-backpropagation.html


CREDITS:
Icons by https://www.freepik.com/

The Most Important Algorithm in Machine Learning

The Most Important Algorithm in Machine Learning

The Most Important Algorithm in Machine Learning

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