Gradient descent, how neural networks learn | Deep learning, chapter 2

Source: http://www.youtube.com/watch?v=IHZwWFHWa-w

Description:

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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://neuralnetworksanddeeplear ning.com/
The book walks through the code behind the example in these videos, which you can find here:
https://github.com/mnielsen/neural-netw orks-and-deep-learning

MNIST database:
http://yann.lecun.com/exdb/mni st/

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:
https://youtu.be/i8D90DkCLhI
https://yo utu.be/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
ht tps://arxiv.org/abs/1706.05394
https://a rxiv.org/abs/1412.0233

Music by Vincent Rubinetti: https://soundcloud.com/vincerubinetti/

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