u/Xiphorianin r/compsci • Recommends
What is backpropagation and what is it actually doing?
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#backpropagation#neuralnetworks#gradientdescent
Backpropagation, intuitively | Deep Learning Chapter 3
3Blue1Brown
8.7M subscribers • 42 videos on Hallway
- Recommended by 5 people: 4 on Hacker News and 1 in engineering communities.
- A classic. People kept recommending it from 2017 to 2021.
- May be out of date. Uploaded 8 years ago, in an area where tools change fast.
Beginner. No background in the topic needed.
6.3M views9 years ago5 recommendations
AI and machine learningComputer scienceNeural networks and deep learningMath for machine learningAlgorithms
An intuitive, visual explanation of how backpropagation works in neural networks during the learning process.
What's actually happening to a neural network as it learns? Help fund future projects: patreon.com/3blue1brown An equally valuable form of support is to simply share some of the videos. Special thanks to these supporters: 3b1b.co/nn3-thanks Written/interactive form of this series: 3blue1brown.com/topics/neural-networks And by CrowdFlower: 3b1b.co/crowdflower Home page: 3blue1brown.com/ The following video is sort of an appendix to this one. The main goal with the follow-on video is to show the connection between the visual walkthrough here, and the representation of these "nudges" in terms of partial derivatives that you will find when reading about backpropagation in other resources, like Michael Nielsen's book or Chis Olah's blog. Video timeline: - Introduction - Recap - Intuitive walkthrough example - Stochastic gradient descent - Final words Thanks to these viewers for their contributions to translations Звуковая дорожка на русском языке: Влад Бурмистров. Italian: @teobucci Vietnamese: CyanGuy111Why it's here
5 people recommend thisWhat people say. Highly recommended as the most intuitive visual explanation of backpropagation and neural network learning.
hal9000xpon Hacker News • Recommends
On “Ask HN: Best way to get started with AI?”
…ples 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 link to this video link So my fear of ML was gone away and I'm very excited to explore whole new world for neural networks and other things like support vector machines etc…
pastelskyon Hacker News • Recommends
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? link to this video His other videos on this topic are just as good.
nouveauxon Hacker News • Recommends
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 link to this video (Original video) link
sxpon Hacker News • Recommends
On “Backpropagation 101 (2020)”
This has a lot of words, but not enough content. I recommend 3Blue1Brown's neural net videos instead: link to this video
seanhunteron Hacker News • Mentions
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 variable1. 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…