Female monkeys can be conscious of the ongoing situation you know ;), plus it could be the other way round as well if not for the centuries of 'I is man, so I is provide for family' prevailing expectations.
Yeah, but the 'you' is clearly referring to a male (who the females are going to 'perform the successful monkey ritual with), so the target is a male reader.
Only within limits. And those limits are as defined by the audience, not by the author. (Or rather, the author, by setting the verbosity, determines the audience. If you want to reach the New Yorker audience, use lots of words. If you want to reach the average HN reader, not so much.)
I agree and thank you for the feedback, for its defense, this was barely the first draft and really skims over the thoughts and follows them as they arrive. It is by no means a revised and generalized piece.
Author here, half an adult i'd say. It was meant to be a stream of consciousness unloading exercise, my first one at that. I have no idea why people are upvoting it.
You're probably getting upvoted because there's a kernel of truth in it. Many of the very intelligent people I know have followed this line of thought at one point or another. I have as well, multiple times.
It keeps coming back to "what's the point of what I'm doing?" and I've personally yet to find any sort of answer that speaks to any sort of higher truth than "I'm here, now what?"
I have a background in Applied Statistics, Applied and Theoretical Physics and Mathematics, with a Masters in Theoretical Physics. Self taught in Machine learning in R and then Python, moving into Deep Learning and Neural Nets. Plan to continue on the same learning curve and break things along the way.
They seem intrigued and eager for further investigation from what i see, the paper showed up on my fb feed through a famous senior physicist sharing it and calling it interesting. ;)
If you haven't already, you should check out the first and second week of Andrew Ng's Coursera Course on Machine learning. He exclusively talks about gradient descent the first few weeks.
https://www.coursera.org/learn/machine-learning
Second the motion. Ang really explains gradient descent very well in that course.
As far as the equations go, if you don't know multi-variable calculus, you might not be able to follow the actual derivations, but I don't think that's all that crucial, depending on what your goals are. Certainly you can apply this stuff without knowing the calculus behind it. And in the ang course, he gives you all the derivations you need to implement gradient descent for various purposes.
Anyway, here's my quick and dirty, way too high level overview of the whole calc business:
All you're really trying to do is optimize (minimize) a function. Given a point on the graph of that function, you need to know which direction to move in in order to get a smaller (more minimal) output. To do that, you calculate the slope at that point. Calculating the slope at a point on a curve is exactly what calculus does for you. If you were working with only one variable, the derivations would be trivial, but once you get into higher dimensional spaces and the need for partial derivatives, that's where the calculus gets a little trickier. But in concept, you're always just doing the same thing... calculating the slope so you know where to move, and by how much (the steeper the slope, the bigger the hop you make in a given iteration).
