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 (Original video) link to this video
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#backpropagation#calculus#neuralnetworks
Backpropagation calculus | Deep Learning Chapter 4
3Blue1Brown
8.7M subscribers • 42 videos on Hallway
- Recommended by 3 people: 3 on Hacker News.
- Presented by Grant Sanderson, creator of 3Blue1Brown
- Linked in the descriptionmnielsen/neural-networks-and-deep-learning
- May be out of date. Uploaded 8 years ago, in an area where tools change fast.
Intermediate. Assumes working knowledge.
3.9M views9 years ago3 recommendations
AI and machine learningComputer scienceNeural networks and deep learningMath for machine learningAlgorithms
Grant Sanderson explains the mathematical foundations of backpropagation using calculus, the chain rule, and partial derivatives to show how neural network weights are updated.
Help fund future projects: patreon.com/3blue1brown An equally valuable form of support is to share the videos. Special thanks to these supporters: 3b1b.co/nn3-thanks Written/interactive form of this series: 3blue1brown.com/topics/neural-networks This one is a bit more symbol-heavy, and that's actually the point. The goal here is to represent in somewhat more formal terms the intuition for how backpropagation works in part 3 of the series, hopefully providing some connection between that video and other texts/code that you come across later. For more on backpropagation: neuralnetworksanddeeplearning.com/chap2.html github.com/mnielsen/neural-networks-and-deep-learning colah.github.io/posts/2015-08-Backprop/ Music by Vincent Rubinetti: vincerubinetti.bandcamp.com/album/the-music-of-3blue1brown Thanks to these viewers for their contributions to translations Звуковая дорожка на русском языке: Влад Бурмистров. Hebrew: Omer Tuchfeld ------------------ Video timeline - Introduction - The Chain Rule in networks - Computing relevant derivatives - What do the derivatives mean? - Sensitivity to weights/biases - Layers with additional neurons - Recap ------------------ 3blue1brown is a channel about animating math, in all senses of the word animate. And you know the drill with YouTube, if you want to stay posted on new videos, subscribe, and click the bell to receive notifications (if you're into that): 3b1b.co/subscribe If you are new to this channel and want to see more, a good place to start is this playlist: 3b1b.co/recommended Various social media stuffs: Website: 3blue1brown.com Twitter: twitter.com/3Blue1Brown Patreon: patreon.com/3blue1brown Facebook: facebook.com/3blue1brown Reddit: reddit.com/r/3Blue1BrownWhy it's here
3 people recommend thisWhat people say. Recommended as an exceptionally clear and visual explanation of the calculus behind backpropagation in neural networks.
hal9000xpon Hacker News • Recommends
On “Ask HN: Best way to get started with AI?”
…hind 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 link to this video 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…
melvinroeston Hacker News • Recommends
On “From scratch: reverse-mode automatic differentiation (in Python)”
…I once made an attempt at animating feed forward propagation 3. If I'd animate back propagation, I'd probably make an animation on autodiffing as well as that is the most intuitive way I know on how to explain back propagation right now. It's still on my big to do list ;-) 1 link 2 3Blue1Brown does in episode 4 of his Deep Learning series: link to this video 3 link…
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…