But what is a neural network? | Deep learning chapter 1
What are the neurons, why are there layers, and what is the math underlying it?
Help fund future projects: https://www.patreon.com/3blue1brown
Written/interactive form of this series: https://www.3blue1brown.com/topics/neural-networks
Additional funding for this project was provided by Amplify Partners
Typo correction: At 14 minutes 45 seconds, the last index on the bias vector is n, when it's supposed to, in fact, be k. Thanks for the sharp eyes that caught that!
For those who want to learn more, I highly recommend the book by Michael Nielsen that introduces neural networks and deep learning: https://goo.gl/Zmczdy
There are two neat things about this book. First, it's available for free, so consider joining me in making a donation to Nielsen if you get something out of it. And second, it's centered around walking through some code and data, which you can download yourself, and which covers the same example that I introduced in this video. Yay for active learning!
https://github.com/mnielsen/neural-networks-and-deep-learning
I also highly recommend Chris Olah's blog: http://colah.github.io/
For more videos, Welch Labs also has some great series on machine learning:
https://youtu.be/i8D90DkCLhI
https://youtu.be/bxe2T-V8XRs
For those of you looking to go *even* deeper, check out the text "Deep Learning" by Goodfellow, Bengio, and Courville.
Also, the publication Distill is just utterly beautiful: https://distill.pub/
Lion photo by Kevin Pluck
Звуковая дорожка на русском языке: Влад Бурмистров.
Thanks to these viewers for their contributions to translations
German: @fpgro
Hebrew: Omer Tuchfeld
Hungarian: Máté Kaszap
Italian: @teobucci, Teo Bucci
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Timeline:
0:00 - Introduction example
1:07 - Series preview
2:42 - What are neurons?
3:35 - Introducing layers
5:31 - Why layers?
8:38 - Edge detection example
11:34 - Counting weights and biases
12:30 - How learning relates
13:26 - Notation and linear algebra
15:17 - Recap
16:27 - Some final words
17:03 - ReLU vs Sigmoid
Correction 14:45 - The final index on the bias vector should be "k"
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Animations largely made using manim, a scrappy open source python library. https://github.com/3b1b/manim
If you want to check it out, I feel compelled to warn you that it's not the most well-documented tool, and has many other quirks you might expect in a library someone wrote with only their own use in mind.
Music by Vincent Rubinetti.
Download the music on Bandcamp:
https://vincerubinetti.bandcamp.com/album/the-music-of-3blue1brown
Stream the music on Spotify:
https://open.spotify.com/album/1dVyjwS8FBqXhRunaG5W5u
If you want to contribute translated subtitles or to help review those that have already been made by others and need approval, you can click the gear icon in the video and go to subtitles/cc, then "add subtitles/cc". I really appreciate those who do this, as it helps make the lessons accessible to more people.
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Transcript
English
3240 words
17742 chars
17 min read
This is a 3. It's sloppily written and rendered at an extremely low resolution of 28x28 pixels, but your brain has no trouble recognizing it as a 3. And I want you to take a moment to appreciate how crazy it is that brains can do this so effortlessly. I mean, this, this and this are also recognizable as 3s, even though the specific values of each pixel is very different from one image to the next. The particular light-sensitive cells in your eye that are firing when you see this 3 are very different from the ones firing when you see this 3. But something in that crazy-smart visual cortex of yours resolves these as representing the same idea, while at the same time recognizing other images as their own distinct ideas. But if I told you, hey, sit down and write for me a program that takes in a grid of 28x28 pixels like this and outputs a single number between 0 and 10, telling you what it thinks the digit is, well the task goes from comically trivial to dauntingly difficult. Unless you've been living under a rock, I think I hardly need to motivate the relevance and importance of machine learning and neural networks to the present and to the future. But what I want to do here is show you what a neural network actually is, assuming no background, and to help visualize what it's doing, not as a buzzword but as a piece of math. My hope is that you come away feeling like the structure itself is motivated, and to feel like you know what it means when you read, or you hear about a neural network quote-unquote learning. This video is just going to be devoted to the structure component of that, and the following one is going to tackle learning. What we're going to do is put together a neural network that can learn to recognize handwritten digits....
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