> Today the computation can take second place to the intuition because we have powerful tools that can take care of all the computation for you.
and that tool is backprop. if you do not understand what the chain rule is and what it is doing, that tool will be magic to you and you are blindly trusting its correctness. seeing that alot of risk is involved in using AI models in real life, blindly trusting your model is not a good approach
i agree that simply regurgitating rules of calculus is pointless to understanding. but thats definitely not what i mean when i talk about the need to understand the chain rule
ML is a mathematically intensive subject. there is no going around this fact
but that's my point. knowing how to compile a program does not make me a compiler engineer. in that sense feel free to use ML tools, but don't be fooled into thinking you will get a job as an ML engineer if you do not know what the chain rule is, or why we need to take a derivative in order to optimise a loss function. in fact, don't even be fooled into thinking you will get into a ML uni degree if you don't know what the chain rule is. i actually don't understand what is the problem. spend 10 minutes reading up on it and i am sure you will get it. i think an unwarranted phobia of mathematics is what is at play here
and that tool is backprop. if you do not understand what the chain rule is and what it is doing, that tool will be magic to you and you are blindly trusting its correctness. seeing that alot of risk is involved in using AI models in real life, blindly trusting your model is not a good approach
i agree that simply regurgitating rules of calculus is pointless to understanding. but thats definitely not what i mean when i talk about the need to understand the chain rule
ML is a mathematically intensive subject. there is no going around this fact