The Complete Mathematics of Neural Networks and Deep Learning by Adam Dhalla
A complete guide to the mathematics behind neural networks and backpropagation.
In this lecture, Adam aims to explain the mathematical phenomena — a combination of linear algebra and optimization — that underlie the most important algorithm in data science today: the feed forward neural network.
Through a plethora of examples, geometrical intuitions, and not-too-tedious proofs, he guides you from understanding how backpropagation works in single neurons to entire networks, and why we need backpropagation anyways.
It's a long lecture, so the author encourages you to segment out your learning time — get a notebook and take some notes, and see if you can prove the theorems yourself.
Adam Dhalla is a high school student from Vancouver, BC, interested in how we can use algorithms from computer science to gain intuition about natural systems and environments.
Link: YouTube
Navigational hashtags: #armknowledgesharing #armcourses
General hashtags: #deeplearning #math #neuralnetworks #nn #optimization #backpropagation
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8April 27, 2026 476 12
