> During the gold rush it's a good time to be in the pick and shovel business
Sure, nvidia is doing pretty well indeed.
Ask HN: Is deep learning obsession in college ill founded?
31–37 of 37 posts
Re: Ask HN: Is deep learning obsession in college ill founded?
#32For your purposes, it doesn't matter. You can do your capstone in the current hotness, or not, as you please. If you do it in deep learning and in five years deep learning is passe, it won't matter. You'll have your degree and be four years into your career. Or you'll be four years into grad school. You'll be fine. (In this scenario, if your grad school is in deep learning, you won't be fine. Think harder about that choice than about your capstone.)
Given all that you've said, a capstone that tries to dent the "inscrutable nature" of deep learning might be an interesting choice.
Re: Ask HN: Is deep learning obsession in college ill founded?
#33Not ill-founded so much as jumping the gun.
To understand why neural networks work, you will have to understand how a whole host of smaller, simpler ML models work in excruciating detail. Multiple linear regression, logistic regression, etc. What they mean, how they work, what's really going on "inside", what the underlying probabilistic model represents, etc.
Neural networks are great because it takes basically all of those smaller ideas and concatenates them into a super flexible statistical machine. It's really cool to see the "in->out" but it's even cooler once you have a good grasp on what's going on in the intermediate steps.
In my experience, almost everyone working with neural networks don't have those details down. This goes 100-fold for non-research roles. They learned the Keras API and are happy stacking layers, and as long as the output looks nice they push to production. For most cases empirical validation is probably enough, because NNs usually can achieve some incremental improvement just by virtue of the fact they have so many damn degrees of freedom. But to get a well-performing, well-founded model, you need to know the ins and outs.
Re: Ask HN: Is deep learning obsession in college ill founded?
#34Earlier quoted context omitted.
While I agree with your sentiment regarding ML engineers - they are just another kind of devs, and that's where it will go - I think DL is not just a tool like any other from the software toolbox. It's more like a paradigm changer, like the print, the engine, electricity, communication and computing. It tends to eat the world.
That's exactly what I'm arguing against: I think the "eating the world" part is a hype-cycle. I think DL has truly revolutionized a handful of very narrow cases - computer vision and speech recognition/synthesis, for example - but that people are vastly over-estimating how "paradigm-changing" it actually is.
yes, but reliable facial recognition alone has huge social implications, never mind all the other potential applications for cognitive automation.
Re: Ask HN: Is deep learning obsession in college ill founded?
#35Personally I think deep-learning is a bubble, and it will soon collapse to its natural place in computer science. Which is not to say that it's a fad that will disappear, only that it will retreat to being just a regular tool among the many tools we have for solving different kinds of problems. Its inscrutable nature is definitely problematic for some use-cases, and not so problematic for others.
NNs are "computer science" only insofar as numerical algorithms are. Which is to say, beyond the question of big-O, it's all math.
Re: Ask HN: Is deep learning obsession in college ill founded?
#36Personally I think deep-learning is a bubble, and it will soon collapse to its natural place in computer science. Which is not to say that it's a fad that will disappear, only that it will retreat to being just a regular tool among the many tools we have for solving different kinds of problems. Its inscrutable nature is definitely problematic for some use-cases, and not so problematic for others.
I've been doing the data thing for a while. During one of my defenses of R, someone brought up that R was a black hole. That if you programmed in R, you were a user who just filled in the correct function arguments and it just spit out the answer. And that was when my thoughts on machine learning changed. The vast majority of us are users. We massage the data to be in a certain shape, then feed it through a machine t…
I consider R to be one of the lower-level ML/DS languages, in that people that use R typically are fairly intentional about what they are doing.
I've been working in this space for a long time and recently started reading up on a particular ML technique which gained a lot of popularity over the past five years. What strikes me about 95% of the material available is how over-hyped and uninformative it is, to the point of just being wrong.
Re: Ask HN: Is deep learning obsession in college ill founded?
#37Earlier quoted context omitted.
While I agree with your sentiment regarding ML engineers - they are just another kind of devs, and that's where it will go - I think DL is not just a tool like any other from the software toolbox. It's more like a paradigm changer, like the print, the engine, electricity, communication and computing. It tends to eat the world.
That's exactly what I'm arguing against: I think the "eating the world" part is a hype-cycle. I think DL has truly revolutionized a handful of very narrow cases - computer vision and speech recognition/synthesis, for example - but that people are vastly over-estimating how "paradigm-changing" it actually is.
Take just computer vision alone. It has applications in manufacturing, robotics, SDCs, medical scans, cartography, agriculture, and many others. It's like the motor - a universal tool.