Backpropagation, intuitively | Deep Learning Chapter 3
Why Hacker News recommends it
Recommended as the most accessible introduction to backprop for people who struggle with the pure math.
What it is
3Blue1Brown's third deep learning chapter gives a visual, intuitive account of backpropagation and what happens to a neural network as it learns. A follow-up video covers the underlying calculus.
Recommended by 4 HN commenters across 3 different months, 2017 to 2024.
What Hacker News said
Strong rec hal9000xp · 2017-11 · on “Ask HN: Best way to get started with AI?”
I'm in the same boat. For long time, I was interested in AI but at the same time intimidated by math. I'm relatively comfortable with discrete mathematics and classical algorithms and at the same time calculus and linear algebra is completely foreign to me. Also, I do not accept way to learn ML without good understanding of core principles behind it. So math is a must. A few months ago, I stumbled upon very amazing YouTube Channel 3Blue1Brown which explains math in very accessible way and at the same time I got feeling that I finally started understanding core ideas behind linear algebra and calculus. Just recently he published 4 videos about deep neural networks: [LINK] [LINK] [this video] [LINK
Strong rec pastelsky · 2018-06 · on “Backpropagation algorithm visual explanation”
While we're at this, I found the explainations by 3Brown1Blue to be very intuitive when it comes to neural networks, especially for folks who're new and don't necessarily grasp concepts when explained primarily through math. What is backpropagation really doing? [this video] His other videos on this topic are just as good.
Recommends nouveaux · 2017-11 · on “What is backpropagation and what is it doing? [video]”
This was very timely for me and for anyone else learning, here are the first few videos of the series: [LINK] [LINK] [this video] (Original video) [LINK]
Recommends sxp · 2021-04 · on “Backpropagation 101 (2020)”
This has a lot of words, but not enough content. I recommend 3Blue1Brown's neural net videos instead: [this video]
Cites seanhunter · 2024-10 · on “What P vs. NP is about”
It's not satire. If you have some function with several inputs, the Jacobian is a matrix of all the partial derivatives of this function with respect to each input variable[1]. Since derivatives give the slope of a function, if you think about your function as being like a bumpy surface with the height at each point being the output, this matrix tells you which way (and how far) to change any input if you want to go "up" or "down" in the output. Backpropogation is a way to optimise a neural network. You want to know how best to nudge the weights of the network to optimise some loss function, so what you do is compute the gradient (ie partial derivative) of that function with respect to each