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Thanks!

I'll give this a listen on the commute to work next week.



Can you explain worldview refactoring? This sounds like something I have been mulling on recently. (Concept not phrase)


https://www.reddit.com/r/dredmorbius/comments/4cudix/21st_ce...

If you take "worldview" to be the aggregate of models, premises, axioms, rules-of-thumb, personal and cultural context, etc., etc., you end up with a pretty hairy melenge of crud.

Some of it is useful and accurate. A lot of it isn't. (All models are wrong, some are useful.) Almost all of it has very strong path dependencies. Language itself is problematic as we define things in terms of other things, and if all the ground is soft and/or unstable, then that shifts.

I'm looking at ideas involving progress, models, institutions, limits, and interactions among them. I've been thinking for a while of adding "values" to that list as well.

There are large constraints in discussing this with others, particularly as much of the thinking is novel (though as I dig through dusty and rusty corners I'm finding I'm reinventing a lot of ideas previously considered -- something I find heartening), but moreso, that those who hold the mainstream worldview are heavily vested in it.

There's an as-yet-unwritten essay on apocalyptic thinking. Not in the sense of fire-from-the-sky the world will explode meaning, but in that the roots of the words "revelation", "enlightenment", "apocalypse", "catastrophe", and "revolution", among others, refer to an overturning. "The scales dropped from my eyes". There's a Cupertino mystic I bumped across online (no, not Steve Jobs) who has a really expressive little bit that enlightenment is not in the least comforting. Or to borrow from Arthur C. Clarke's title -- it's a childhood's end.

Solidifying a bit, I'm finding the ecologists far more convincing and reality-based than (most of) the economists. Howard Odum, William Ophuls, Vaclav Smil, Nicholas Georgescu-Roegen (economist), Herman Daly (ecological economist). But the roots of economic thought, in particular 18th and 19th century, are also pretty fascinating. There's Steuart (almost wholly ignored by Smith), and as a recently discovered example, T. E. Cliffe Leslie, "The Political Economy of Adam Smith" (1870), which does an excellent job of expressing what I've been trying to put my finger on about economics (and all philosophy) being grounded in specific circumstances.

http://socserv.mcmaster.ca/econ/ugcm/3ll3/leslie/leslie01.ht...

No branch of philosophical doctrine, indeed, can be fairly investigated or apprehended apart from its history. All our systems of politics, morals, and metaphysics would be different if we knew exactly how they grew up, and what transformations they have undergone; if we knew, in short, the true history of human ideas. And the history of political economy, at any rate, is not lost. It would not be difficult to trace the connection between every extant treatise prior to the `Wealth of Nations,' and conditions of thought at the epoch at which it appeared. But there is the less occasion, for the purpose of these pages, or of ascertaining the origin and foundation of the economic doctrines of our own day, to go behind the epoch of Adam Smith, that he has himself traced the systems of political economy antecedent to his own to a particular course of history, to `the different progress of opulence in different ages and nations,' and `the private interests and prejudices of particular orders of men.' What he did not see was, that his own system, in its turn, was the product of a particular history; that what he regarded as the System of Nature was a descendant of the System of Nature as conceived by the ancients, in a form fashioned by the ideas and circumstances of his own time, and. coloured by his own disposition and course of life. Still less could he see how, after his time, `the progress of opulence' would govern the interpretation of his doctrines, or how the system he promulgated as the system of liberty, justice, and divine benevolence, would be moulded into a system of selfishness by `the private interests and prejudices of particular orders of men.'

Also claimed is that Smith is entirely deductive. I'm not so convinced of this (Smith actually does cite observations and behaviour frequently), though it is true in parts, and most true in those parts of Smith that are most widely and loudly championed today, particularly the wholly fabricated notion that an "Invisible Hand" is some mechanism by which markets operate. It's not mechanism but apparent outcome, an outcome, it turns out from much subsequent studies. fantastically sensitive to actual conditions, and not in the least guaranteed. (See epecially Joseph Stiglitz.)

Anyhow, small bits. But yes, I'm running an abattoir for sacred cows and received truths.

The linked subbreddit (above) is where I'm exploring much of that. Conversation spills out in other places, including HN.


Event bus mostly


Sorry this is off topic.

Is there a way into machine learning without doing going and going an Ms or PhD?

Im working my way through a coursera course on it but I'm not sure that is enough. All the positions I see are looking for academic experience or 5+ years doing it. Neither of which are doable for me.


If you know almost nothing about the field, then introduction to statistical learning is a good choice.

http://www-bcf.usc.edu/~gareth/ISL/ISLR%20First%20Printing.p...

It assumes some understanding of calculus, but doesn't require matrix algebra.

The original (and amazing) book that lots of people used is Elements of Statistical Learning.

https://web.stanford.edu/~hastie/local.ftp/Springer/OLD/ESLI...

Chapters 1-7 are worth their weight in gold. This is one of the cases where the physical books are much better, as you'll need to flick back and forth to see the figures (which are one of the best parts).

The forgoing assumes that you already know some statistics/data analysis (the latter probably being more important).

If you haven't done this before, then I suggest that you acquire some data you care about, install R (a good book is the Art of R Programming by Matloff), and start trying to make inferences. And draw graphs. Many, many, many graphs.

If you keep at this, finding papers/books and reading theory, and implementing it in your spare time, then you can probably get a good data science job in 1-2 years. You'll probably need to devote much of your free time to it though.

I'm assuming that you can already code, given the context :)


Thank you for this, i really appreciate you sharing these resources.


I'm on that coursera course too! The course is pretty basic though. It'll help you get the concepts but there's too much spoon feeding in there to make you good enough to compete with people with MS and PhDs. Also that course doesn't cover deep learning and you should definitely study that.


I don't know how reproducible the approach is, but i'm working my way in from being a php developer previously. The company i work for is building a big data / machine learning platform from the ground up, and they bootstrapped the project from existing employees, including myself.


I want to like Duolingo more but I don't understand how you can work your way through the course if your keyboard doesn't have foreign characters on it. I tried to get into learning Russian briefly but gave up on the first 20 minutes as I kept failing the tasks that requires me to type Russian characters.

Was I doing it wrong?


a) You don't have to type Russian characters. There's a little switch that lets you input everything in English transliteration. b) There's an explanation right at the beginning on how to install a Russian keyboard layout: https://www.duolingo.com/comment/11449014


You can try downloading a virtual keyboard in Russian that works better. Many free resources on the net.


Thank you for sharing this. Fascinating read. Do you have any similar material?


If you could make it so when ever company X is hiring one or more staff you could share the location of these hires in the link. I would appreciate it thanks.


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