Coding a Neural Network: A Beginner's Guide (part 2)

Neural networks simplified and made easy, for the layperson (or medical practitioner). I've tried to keep things simple, and provide a beginner's introduction to machine learning and neural networks. By the end of this series, you'll have created your first complete and functioning artificial neural network, written in Google Colab. I recommend listening on 1.5 or 2x speed.
Part 2 is all about generating a 'loss function', which will help us determine how poor our network's predictions probably were.
Part 1: • Coding a Neural Networ...
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Part 3: • Coding a Neural Networ...
Part 4: • Coding a Neural Networ...
Part 5: • Coding a Neural Networ...
This tutorial is greatly indebted to the work of Justin Johnson: github.com/jcjohnson
Let me know what you think in the comments below👇

[@jennahvieve]

Ty!

[@amongus_pvp]

TYSM

[@yuveshragavendiran]

Very goood loved it.though it takes time ,as ur videosare of great potential , they will get famous!!!

[@bartlomiejkowalczyk4673]

amazing, do you have any course about deep learning?

[@MWKnives]

Correct me if I'm wrong, but isn't the result of multiplying the result of the ReLU function by the second weights supposed to be o_nodes? You defined this variable in the context of making it align with the visual graphic. This should be the output of that computation, right? It gets a little confusing following you when you populate what is supposed to be the output of the network with random data. I think you simply meant to assign o_data instead of creating a whole new variable.

[@ncsources9835]

I do minecraft coding stuff.

[@TheAstrocricket]

nice