Gradient descent, how neural networks learn | Deep Learning Chapter 2

Cost functions and training for neural networks.

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Written/interactive form of this series

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

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

MNIST database:

Also check out Chris Olah's blog:

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:

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

"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:

Music by Vincent Rubinetti:

Thanks to these viewers for their contributions to translations

Звуковая дорожка на русском языке: Влад Бурмистров.

Hebrew: Omer Tuchfeld

Italian: @teobucci

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