The government's reaction to server host Carpathia refusing to destroy all the evidence that Dotcom claims exonerates him is to announce that they are considering filing criminal indictments against Carpathia.
> The government's reaction to server host Carpathia refusing to destroy all the evidence that Dotcom claims exonerates him is to announce that they are considering filing criminal indictments against Carpathia.
That is absolutely disgusting.
Let me get this straight - their logic is 'These guys did something illegal, and you have evidence that they didn't commit the crime in your possession. You need to destroy the evidence, because we're planning on charging you as an accomplice to that same crime.'
Aside from algebra to do log or exp division addition and multiplications, you have to be versed in statistics. Most ml problems are solved on statistical bases. Although many of the algorithms have been solved, you still need to grasp the statistics behind it which is a bit more involved than calculating the odds of a die
Yes, many people that I know working on ML do not remember statistics well enough. Some keep their ignorance while relying on mathematicians on-staff (who can't really code), while others either start buying college textbooks or go to night classes. There are too many people who don't understand the algorithms being used.
To work with ML stuff you don't need much. You can just download packages and get experience with how to best pick models, choose features and tweak (hyper)parameters. If you want to work on or understand it then you will need math.
You need a decent understanding of calculus (mid 1800s level, mulitvariate calculus), a more decent understanding of Linear Algebra (1950s ), information theory (1960s), and probability and statistics. With the last having shifted the most from the past due to more recent respect for bayesian methods. Note the years in parenthesis is not to say that nothing new has been used from those areas, more like if you pick up a book on that topic from that year you would be pretty well covered for the purposes of ML.
Worth having a vague idea of are stuff like PAC learning, topology and computational complexity stuff like Valiant's work on evolvability. If you are doing stuff related to genetic programming then category and type theory have riches to be plundered.
Or if you want to be more hardcore and are looking at very higher dimensional data and reductions on them you might look at algebraic geometry (in particular algebraic varieties) and group theory. So basically the answer to your question is as little or as much math as you want and or depending on the problem and your interests in trying different approaches than the typical toolkits of linear algebra and statistics.
* If you are doing stuff related to genetic programming then category and type theory have riches to be plundered.*
Could you expand this a bit as I don't understand the meaning. Are you saying that if the problem you are working on can be solved with genetic algorithms, then you could blow it away with category and type theory?
I don't have a vested interest in either, I am just curious. Thanks.
This was a great article considering the issues I've been having with my fellow co-founders.
It's become clear I'm not respected, and get that 'just a developer' treatment. Perhaps thats my fault for not being more outspoken and critical of others. Either way, if there isn't mutual respect and trust, it ain't worth it. Its going to make you miserable.
Another point is that the CEO can't bring down the pressure on the slackers. The slackers can't directly admit to being slackers. And the CEO is taking 51% and not in favor of investors (wants to self-fund).
This has been a nightmare. Hopefully not having assigned my software over will protect me.
I've learned the hard way the consequences of jumping in bed with co-founders without making sure that there is alignment on many of the points in the article.
What are the disadvantages of 3rd party services? Are you really giving away secrets to 3rd party services?
I'm with you on the communication issue. After all, no one is 100% introverted. That term has also become misunderstood and misused.
Additionally, there tend to be other negative qualities associated with introverts. Just as there are negative qualities for extroverts.
One thing I did recently that's helped me deal with being an 'introvert' was to take improv comedy classes. It's helped me significantly to overcome shyness and communicating with people.
Communicating is a like a muscle, you need to step out of your comfort zone and just do it. Staying in the 'cave' too much at a time isn't good. It's sorta like atrophy. Just go out once a week and socialize or go to lunch with a co-worker.
"I'm with you on the communication issue. After all, no one is 100% introverted. That term has also become misunderstood and misused."
Yet you still seem to think that introversion and communication skills have something to do with each other ('after all ---'). I don't think this is true. The fundamental difference between introversion and extraversion appears to be how people 'spend' energy and how they 'gain' energy.
For example, I'm probably as introverted as they come. Yet in social situations I seem like an 'extraverted' person, because when I'm with people, I like to talk quite a bit, have some social chatter, all that stuff. But this can tire me. And then there are months when I don't go out and socialize, simply because I'm tired and like to be alone with my thoughts. None of this has much to do with skills of any kind.
The lines have blurred because the WWW was by far the most important and most visible system/protocol driving development of the internet.
I think it's a bit like internal combustion engines: They existed before cars, but people associate motors with cars, and even companies named themselves after them "Ford Motor Company", GM, etc... They became synonymous.
It seems like you're making the assumption that 'they' would choose nginx just because it's superior.
There are many reasons for them not to go with nginx. The biggest one is probably that it's just different. Managing it is different. Configuring it is different (regardless of any similarities). Testing is still required.
It's nothing new that a lot of people either want to be lazy or are afraid of taking risks.
Being 'better' alone isn't a good enough reason to expect everyone to switch.
Different is risky. Consider, for example, lighttpd, another lightweight Apache alternative. It was chosen for a certain project at a former workplace of mine. Not too very long afterward, significant problems with memory leaks started occurring on the deployments that used it, and I found myself tasked with fixing them.
I was unprepared and somewhat astounded to find that lighttpd does not support sending "large files" over CGI, FastCGI, or proxy connections, and the maintainers don't care (cf. http://redmine.lighttpd.net/issues/1283 ...)
Besides the waste of engineering time, customers were impacted.