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I found that in the ML Class, we could complete many of the assignments by doing the calcs via "for-loops" in Octave - and as you noted, it was really slow.

But I think Ng wanted us to understand what was going on under the hood in Octave when you used its in-built vector primitives, and how to think about the problems in such a way to understand how to "vectorise" them so that the solutions would be amenable to using those primitives.

There was a time where I wasn't "getting it" - and proceeded with my own implementation. In time though, it "clicked" for me, and I could put away those routines and use the vector math more fully (and quickly). That said, I wouldn't have wanted it to be left as a "black box" - it was nice to have the understanding of the operations it was doing behind the curtain.

Which is also why I was disappointed that the math wasn't described more in either of those courses; that part was left as "black boxes" and only hinted at a bit (ie "here's the derivative of the function - but you don't have to worry about it, but if you know about the math, you might find it interesting").

In this latest course I am taking, though, they are diving right into the math - and I have found that they assume you already know what a derivative is and how it is formed from the initial function. Unfortunately, I don't have the education needed, so I am fumbling through it (and doing what I can to read up on the relevant topics - I even bought a book on teaching yourself calculus which was rec'd for me by a helpful individual).

Yes, my point is that even though numpy provides a "black box" set of functions, using them is so unnatural (at least for me) that I was forced to completely understand what the numpy functions were doing internally to have any hope of using them.

In fact, now that I think about it, the first assignment asked us to write a 2-loops-in-python version of some function (batch linear classifier, I think), then a 1-loop version, then a 0-loop version, and I often repeated this procedure on subsequent harder questions.

That was an useful thing I wasn't expecting to learn from the course - how to vectorize code for numpy (including using strange features like broadcast and reshape)

If I can offer some advice as a former Calc I student, since you are focused on ML, ignore integrals and the fundamental theorem of calculus. Instead, understand the connection between derivatives and antiderivatives and become practiced with the rules of derivation: product rule, quotient rule, trigonomety functions (tanh is part of hyperbolic trigonometry), exponentiation, logarithms, and the chain rule. Using derivatives to find local minima and maxima will also be useful. You should be able to look at the graph of a function and quickly visualize the graph of its derivative.

That's why I enjoyed the "hackers guide to neural networks" so much - it builds everything from the ground up

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