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And how about the indigenous Inca & Maya people, nobody wants to see their human sacrifice culture celebrated on Facebook (Mel Gibson exposed the ugly truth in Apocalypto)

And the Spanish Inquisition, Bluebeard, Ivan The Terrible, Robespierre, Lenin, Beelzebub and Donald J. Trump?

The ban list is way too short.


> And how about the indigenous Inca people, nobody wants to see their human sacrifice culture celebrated on Facebook (Mel Gibson exposed the ugly truth in Apocalypto)

Apocalypto focussed on the Maya, who aren't even from the same continent as the Inca.


You’re right I always mix them up, corrected


With the exception of Lenin, I don't think contemporary political positions hold a great deal of water for any of these people. The list probably isn't an objective scale of atrocity, it's probably proportional to the volume of content that Facebook actually sees.


Arguably, time is better spent studying nuclear engineering.

That's the only thing that can save the planet (besides enabling space exploration and colonization of Moon and Mars)


Not necessarily. Nuclear energy gives GHG-free power, but 1) doesn't deal with the priced-in impact on the biosphere that will require us to change a lot of things 2) doesn't reprogram societies to become less impactful.


How exactly is fucking up some other celestial bodies going to help save this one?


gives us time and space to continue evolving without the impending disruptive setback. there is also enough free floating stuff out there it could become not worth it to disrupt the top few inches of land on this planet which is where all the irreplaceable magic happens


Some believe humans should strive to become multi-planetary species, e.g. to avoid a possible extinction event.


there is not much math, don't let the academic papers razzle dazzle you, they make things look complicated to get their grants

in practice, as long as you've studied basic calculus and understand how to find a minimum of a function via derivative you're good, there is your "gradient descent" in a nutshell: https://www.mathsisfun.com/calculus/maxima-minima.html

everything else is plug-and-play from existing libraries

you can ask any "data scientist" or "ML engineer" what they do all day, it's a whole lot of copy paste, and tweaking the data and parameters through trial and error until it fits

Edit: Ok , it would also help to understand dimensionality reduction via PCA/SVD at least once, it's available in any linear algebra book: https://en.wikipedia.org/wiki/Singular_value_decomposition , https://en.wikipedia.org/wiki/Principal_component_analysis that's probably the best and most "scientific" part of ML


Thank you! appreciate your reply, honestly. i have an engineering degree but it has nothing to do with computers, so i kinda struggle sometimes. I basically want to get fluent with the metrics/performance when applying ML/NN, when to tweak what, how to improve some stuff, what algorithm works best for some of the problems, etc.


This, in a nutshell.


If you want to understand how the brain works this is a good intro with some realistic neuronal network models ( spoiler: these have nothing to do with “artificial neural nets” as we know them) https://www.amazon.com/Brain-Computations-Edmund-T-Rolls/dp/...


Soon they will start a popular startup incubator and change VC investing as we know it


Ben Franklin did something similar, he copied books by hand I think


I think he rather took short notes, then attempted to rewrite the material based on the notes, comparing to the original afterwards. This would seem to me to be much more effective than copying directly out of the book when it comes to recall.


we are just worth more


Not really, the same person is worth more for the same work if they're doing it in SF versus London.


Big fan of your spellchecker


Databricks IPO is upcoming, with valuation of ~40B.

But I predict it will be sold to either Microsoft, Amazon or Salesforce within a year or so, may be sooner.


That’s correct understanding as of 1943 when the “artificial neural network“ model your professor is teaching was developed.

There is a whole lot of new knowledge on how live neurons and networks of neurons work that had been collected in the last 75 years in the neuroscience domain but it’s mostly ignored by computer scientists.


I’d be really interested in learning more about this. Can you point me to some easily grokable literature?



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