2017-10-16

[public] 3.82M views, 147K likes, 619 dislikes audio only

3Blue1Brown

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Help fund future projects: https://www.patreon.com/3blue1brown

Special thanks to these supporters: http://3b1b.co/nn2-thanks

Written/interactive form of this series: https://www.3blue1brown.com/topics/neural-networks

This video was supported by Amplify Partners.

For any early-stage ML startup founders, Amplify Partners would love to hear from you via 3blue1brown@amplifypartners.com

To learn more, I highly recommend the book by Michael Nielsen

http://neuralnetworksanddeeplearning.com/

The book walks through the code behind the example in these videos, which you can find here:

https://github.com/mnielsen/neural-networks-and-deep-learning

MNIST database:

http://yann.lecun.com/exdb/mnist/

Also check out Chris Olah's blog:

http://colah.github.io/

His post on Neural networks and topology is particular beautiful, but honestly all of the stuff there is great.

And if you like that, you'll *love* the publications at distill:

https://distill.pub/

For more videos, Welch Labs also has some great series on machine learning:

/youtube/video/i8D90DkCLhI

/youtube/video/bxe2T-V8XRs

"But I've already voraciously consumed Nielsen's, Olah's and Welch's works", I hear you say. Well well, look at you then. That being the case, I might recommend that you continue on with the book "Deep Learning" by Goodfellow, Bengio, and Courville.

Thanks to Lisha Li (@lishali88) for her contributions at the end, and for letting me pick her brain so much about the material. Here are the articles she referenced at the end:

https://arxiv.org/abs/1611.03530

https://arxiv.org/abs/1706.05394

https://arxiv.org/abs/1412.0233

Music by Vincent Rubinetti:

https://vincerubinetti.bandcamp.com/album/the-music-of-3blue1brown

Thanks to these viewers for their contributions to translations

Hebrew: Omer Tuchfeld

Italian: @teobucci

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Video timeline

0:00 - Introduction

0:30 - Recap

1:49 - Using training data

3:01 - Cost functions

6:55 - Gradient descent

11:18 - More on gradient vectors

12:19 - Gradient descent recap

13:01 - Analyzing the network

16:37 - Learning more

17:38 - Lisha Li interview

19:58 - Closing thoughts

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