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: Icons by
Hide player controls
Hide resume playing