"7 habits of highly effective people" was one of the books that I read almost 12 years ago and have skimmed through many times since. As time passed by, things that I couldn't absorb the first time around became more apparent. The book was as profound and relevant when it was written as it is today. RIP Stephen.
A lot of people used to carry pens on their shirts. Many still do. Now using bluetooth earpieces is commonplace. I am sure people will get used to using very tiny gadgets on their clothing or ears if they assist them in meaningful ways.
I really don't see as much earpiece fashion anymore. To me it always screamed "extra device" which is the opposite of luxury or convenience. Or maybe I'm just blind to a common trend now.
If it works well enough, of course it will be cool. Once everyone has one to get shit done with, "cool" will be one of the biggest differentiators to determine which one to get.
If it doesn't work well enough, then it will be the new Newton—and even then, here we are 20 years later, and tablets are cool.
How does one get control over the predictive model? What classifier gets used, for instance. Maybe there is something in the API, but I didn't see it in the article.
We provide basic classification and regression trees for now, and we can decide which one is appropriate from the objective field type. Once we start adding in other types of models we will add a model type parameter for the relevant API method.
Do you plan on exposing parameters that control the fitting process? E.g. loss function / tree depth / min samples per leaf? Or will the fitting process always be a black-box automagic call with no user-controllable knobs?
Is there any plan to provide some assessment of model accuracy via the API - e.g. K-fold cross validation with respect to some specified loss function?
We do a little automagic currently, but we'll expose some of the knobs soon, probably first via the API. Expressing model confidence and handling loss functions are being worked on right now.
Did I mention we're hiring? Someone with the right combination of big data and machine learning skills can make a big impact.
https://bigml.com/team
What you make will probably a lot less than the founders. But if the company is successful, that will still be quite a lot more than what you get from a regular salary working at a big company. And what you end up learning is also quite a lot more.
So, if you are not starting your company and if you believe in what the founders are doing, I would say go for it.
Thank you for the response. I also have an startup idea I'm excited about, but its chances of success are much more unknown than this existing startups. The founders are pretty well connected, so it seems I'd be learning a lot of things that I might not if I were the founder the first time around.
Python holds a strategic middle ground. It's far more productive, expressive and concise than the traditional procedural and OO languages. Although not as expressive and elegant as the new FP languages like Clojure and F#, the latter lack massive libraries and modules that have been written for Python. That makes them useless for any 'real' work.
Python is seeing some heavy use by the scientific community due to tools like NumPy, matplotlib, libsvm and what not. Good luck finding that range of independent libraries for a new FP language. I am not dropping Python in the near future, but I am watching.
This is actually excellent advice, although may or may not be harshly presented depending on your perspective. Setting goals are probably the single best way to learn anything and get sharp in the process.
I am a CS grad and am 30 now and have tried to learn several things in the past that have interested me in a variety of areas. I remember a few examples where I would learn/pickup/read something fast and think that I understand it but I would truly understand it only when I took up an ambitious project that involved learning and applying something. And your mind is automatically focussed/sharp in picking up stuff when there is a goal in front. Otherwise, it may just feel like cramming. Getting sharp should be a by-product of doing something worthy and ambitious that is aligned with your interests.
Also, don't worry about mediocrity just as yet since you're just starting out. But I've seen a lot of guys getting cozy in a couple of years after they graduate and get a job. So, do keep lofty and aligned goals in mind all the time. You will surprised. And that will take you way above mediocrity while keeping you sharp and vibrant as a person.
