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 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 ----------------- 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" ------------------ Animations largely made using manim, a scrapp