Basics of Deep Learning Part 13: Implementing the Backpropagation Algorithm with NumPy

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In this series we are going to cover the basics of deep learning. And in this video we are going to implement the backpropagation algorithm in code.

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If you are wondering why the slides don’t disappear even though I am typing in the jupyter notebook, I used AutoHotkey for that. Here is an article that describes how to use it:

Sebastian Mantey

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Thank you for explaining each and every small concept in details. This is a must watch series for anyone who wants to learn to implement neural networks from scratch.

SouravGarai

Actually, your videos are much more helpful than my ML class :-)))
My Prof. on ML class would be jealous.

jcoixgz

excellent video, straightforward and to the point, dankeschön m8

branoraguz

Thank you so much for sharing this awesome course. i really appreciate you describing everything from scratch. It really helped me understand the math behind backpropagation. Most other courses on youtube don't go into that much detail unfortunately. And I also really like that you explain the intuitive understanding of deep learning. I played around a bit with your code and added some additional things: the bias, a weight initialization and a relu function. And the softmax for cross validation, which was very important for me. But I think you left that out on purpose so as not to confuse us, which I appreciated.

riccinef

This is some extra ordinary content ..mate ...

MrAnandml

Why we use sigmoid? Isnt on the multi class problem we use softmax? Please answer please i really confusseeee

FaktariaID

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