> So while this looks like a great resource - who’s it for?
I give you an analogy. Electricity. Who needs to know complex numbers and differential equations to understand electricity? Technician, civil engineer, scientist or research engineer?
Technician who just wires the house don't need math. They just read the wiring instructions and follow standard practices. Nvidia boasts about the tools it builds for 'ML technicians' in this analogy.
You need to know math if you are building new architectures and applying complex models for something nontrivial. It's not going to work first time and you need to know what's going on. Even if you are the 'civil engineer' in this analogy you should be able to read the math and understand it even if you don't do the math by yourself. You won't be able to do literary research and learn new stuff if you can't read math fluently.
If you are programmer who is given ML tools to implement something someone else designed and understand you don't need this or use existing models, you don't need this. Your career might benefit from knowing it but you can manage without.
I believe the OP's point was that the math described in the article is too simple and not enough to do any serious research. Anyone who attempts to do NN research already knows this material (and a lot more). This tutorial could be useful to someone who wanted to implement simple backprop from scratch, but all DL libraries already do it automatically. Someone who just wants to learn a bit about NNs to classify images or generate text does not need to know this, and someone who wants to make a breakthrough in NN theory already knows it. So yes it's not very clear who is the target audience here. I'm guessing it's for a bright highschooler who just learned calculus and who is interested in how NNs work. For such students I'd recommend reading http://neuralnetworksanddeeplearning.com instead.
But someone who wants to contribute to the research doesn't just have this knowledge pop into their mind out of nowhere. They're going to learn it from somewhere and what's wrong with one more resource to help out with that.
I give you an analogy. Electricity. Who needs to know complex numbers and differential equations to understand electricity? Technician, civil engineer, scientist or research engineer?
Technician who just wires the house don't need math. They just read the wiring instructions and follow standard practices. Nvidia boasts about the tools it builds for 'ML technicians' in this analogy.
You need to know math if you are building new architectures and applying complex models for something nontrivial. It's not going to work first time and you need to know what's going on. Even if you are the 'civil engineer' in this analogy you should be able to read the math and understand it even if you don't do the math by yourself. You won't be able to do literary research and learn new stuff if you can't read math fluently.
If you are programmer who is given ML tools to implement something someone else designed and understand you don't need this or use existing models, you don't need this. Your career might benefit from knowing it but you can manage without.