I completely get the value of these sessions. It is beyond my ability to put it in words how much the idea of operating efficiently on data resonates with me.

APL, the Einstein notation in numpy, this is where itβs at.

But one thing that is quite cool that happened to me as a result of taking the fastai course is that I went from spending 47 days on reading a single ML paper to being able to grok what a paper is about in 3 or 4 minutes.

This allows me to interact with the field in a completely different way, I am not intimidated by what I find on arxiv anymore.

I have never known this side to Jeremy, but he strikes me as a mathematician. Math is very real to him. For me, I have not crossed that divide. I have not thought about maths for years now and participating in the APL walk-throughs is akin to loading back up all that math information from old and scratched vinyl plates.

My silly misconception is that I would like to be able to relate to mathematics the way I Cab relate to ML papers. To feel at home. To be able to breeze through them and to go down a rabbit hole of my own choosing to an arbitrary depth.

Now, this is not something that is likely to happen. But if I were to chart such a path for myself, to understand math at a visceral level, I would probably opt for being adopted by Jeremy This cannot happen though since I am over 18 years old.

But another slightly less likely to succeed path would lead through this coursera course. And of course, following through with attending the APL sessions. I will likely not retake the coursera course. It is one of these thoughts that you entertain but then you become faced with having to do groceries or do something for work and simply give up due to priorities. But that course was truly a wonderful experience introducing an outsider into the world of math notation, and what mathematical reasoning is about.

I would also like to add that I did learn about complex numbers and the ideas behind what `loss.backward`

can give me literally in an instant. But I also spent years trying to learn this stuff on my own. And the extent of knowledge Jeremy is able to transmit via off-the-curf remarks is unbelievable. For instance, the insight that complex numbers add another dimension to the real number line, that they are nothing more than us agreeing that the number `i`

is a number whose square root is -1. Wow. This captures so many applications in engineering, so many things I barely scratched the surface of in my own study that took literally months if not years. And yet it takes Jeremy 5 minutes to deliver this insight with hand-drawn graphics. Seems to me like Jeremy might be on the verge of democratizing yet another area, making maths uncool again

Anyhow, this message might be weird, but I canβt put in words how grateful I am for being part of the fast.ai community and being able to learn from Jeremy.

And last by not least, I would like to share this video with you. Now that I have come across as a complete newb in one of the walk-throughs, claiming that βmath that we are learning about is just definitions arrived at by humansβ, here is a couple of words from Richard Penrose, a noble prize laureate, speaking to how mathematics is not really invented but discovered. And how precise math is in describing the world around us. Because when you think about it, there is really no reason why math should align with the world, and yet it so beautifully does.

I am committed to being the weird member of the fast.ai community And sharing my weird thoughts. Very grateful for being on this journey with all of you! And grateful to be the newb yet again. There is no state in the universe Iβd rather experience, apart form the very depressing predicament of there being value in convincing other people you actually are a `non-newb`

when it comes to certain matters. Oh well.