41–50 of 119 posts
How can there be experts on a subject that doesn't yet exist?
Earlier quoted context omitted.
You just interview people. Ask them to explain what a SVM is. Ask them to explain how training a linear perceptron works. This kind of stuff.
Sketchy, but in the other direction. In high school I did a project that involved a support vector machine, and over the course of this I learnt how they worked to a reasonable level of detail. Two years later, I interviewed at a large SV company, and they asked what projects I'd been working on. I gave a description of this project (2 years prior) and my explanation was phenomenal; in order to understand SVMs two ye…
This is suspiciously close "data science" and "machine learning" experts. Can't we just be honest and say that most of these are applied statistics jobs with a specialty in large volumes of data? Or is "statistics" just not fashionable enough nowadays?
Earlier quoted context omitted.
Sketchy, but in the other direction. In high school I did a project that involved a support vector machine, and over the course of this I learnt how they worked to a reasonable level of detail. Two years later, I interviewed at a large SV company, and they asked what projects I'd been working on. I gave a description of this project (2 years prior) and my explanation was phenomenal; in order to understand SVMs two ye…
You're pretty harsh with yourself. You showed that you were interested in the topic and that you could learn it by yourself. I don't think anyone expect an intern to already master the subject he'll be working on, picking a smart motivated person is usually what you're looking for.
AI is very interesting but not very accessible because it's so specialized. I have a fairly strong programming background but feel like I'd need to study theory for a significant amount of time to even get my feet wet with AI. If you have (condensed, especially) AI resources that you think would help bridge that gap, please share! Toy-scale project ideas would also be appreciated.
There was a tutorial link trending on HN a few months back, I can't seem to find it. But these links are helpful (though not condensed) as well: https://github.com/ChristosChristofidis/awesome-deep-learnin... https://github.com/owainlewis/awesome-artificial-intelligenc...
Earlier quoted context omitted.
You're pretty harsh with yourself. You showed that you were interested in the topic and that you could learn it by yourself. I don't think anyone expect an intern to already master the subject he'll be working on, picking a smart motivated person is usually what you're looking for.
Ah, but if you combine it with the fact that when I did study those things I didn't really enjoy them, you can probably see why I look back on the experience with healthy scepticism :P
(If you didn't, maybe the interview worked...)
Apologies in advance for the bluntness, and don't feel like you need to answer.
Earlier quoted context omitted.
Ah, but if you combine it with the fact that when I did study those things I didn't really enjoy them, you can probably see why I look back on the experience with healthy scepticism :P
The question I'm more interested in: when you worked there as an intern, did you suck? (If you didn't, maybe the interview worked...) Apologies in advance for the bluntness, and don't feel like you need to answer.
This is suspiciously close "data science" and "machine learning" experts. Can't we just be honest and say that most of these are applied statistics jobs with a specialty in large volumes of data? Or is "statistics" just not fashionable enough nowadays?
AI is very interesting but not very accessible because it's so specialized. I have a fairly strong programming background but feel like I'd need to study theory for a significant amount of time to even get my feet wet with AI. If you have (condensed, especially) AI resources that you think would help bridge that gap, please share! Toy-scale project ideas would also be appreciated.
(source: have tried to 'bridge the gap' for 2 year, including taking MSc courses, before admitting to myself that it's a lost battle. Am now starting to build a solid math foundation before revisiting ML applications.)