But what is a Neural Network? | Deep learning, chapter 1
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Typo correction: At 14 minutes 45 seconds, the last index on the bias vector is n, when its supposed to in fact be a k. Thanks for the sharp eyes that caught that!
For those who want to learn more, I highly recommend the book by Michael Nielsen introducing neural networks and deep learning: goo.gl/Zmczdy
There are two neat things about this book. First, its available for free, so consider joining me in making a donation Nielsens way if you get something out of it. And second, its centered around walking through some code and data which you can download yourself, and which covers the same example that I introduce in this video. Yay for active learning!
github.com/mnielsen/neural-networks-and-deep-learning
I also highly recommend Chris Olahs blog: colah.github.io/
For more videos, Welch Labs also has some great series on machine learning:
youtu.be/i8D90DkCLhI
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: distill.pub/
Lion photo by Kevin Pluck
— 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
— Animations largely made using manim, a scrappy open source python library. github.com/3b1b/manim
If you want to check it out, I feel compelled to warn you that its 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.
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