31–36 of 36 posts
Look exciting. Realistically, what can one expect to learn from the class? Would I learn any practical skills or is it just an overview?
I feel like there's about 1000 hours of high quality AI lectures available for free on the internet and while I do believe in a certain amount of selflessness in education, I am skeptical that any of that is providing more than a glimpse of what you need to know to be productive at it. In other words, there's a thousand hours of material out there, which probably takes 10000 hours to actually get into so you might as…
Studying it at a university means moving and having a certain background. While i agree that these resources are hard to break down into a curriculum, theres nothing stopping you from copying a university curriculum at home and doing work on your own...
Yet most people don't. Same for learning an instrument, carpentering etc.. While you could in principle self-study lots of things, study groups, structure, people to talk to and discuss with and even just "we meet every Thursday at noon to ..." are not negligible.
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Studying it at a university means moving and having a certain background. While i agree that these resources are hard to break down into a curriculum, theres nothing stopping you from copying a university curriculum at home and doing work on your own...
> theres nothing stopping you Yet most people don't. Same for learning an instrument, carpentering etc.. While you could in principle self-study lots of things, study groups, structure, people to talk to and discuss with and even just "we meet every Thursday at noon to ..." are not negligible.
Indeed. These resources are stupendously valuable, and probably somewhat easier to find/generate in some areas than most others.
The gap that I see in current machine learning is that everyone is learning how to use the popular models, but no one knows how to construct a new model that solves a new problem. So everyone can download word vectors and use them for what they're good at, but the second you get off the beaten track, almost all machine learning practitioners fall flat. I really dont think this is due to how new the field is, rather t…
One needs to form an experimental design with a ability to detect the challenge, understand the properties and come up with an appropriate computational solution to that challenge. This isn’t just for Machine Learning but for any kind of algorithm you develop.
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ML is a very applied subject. There is entirely too little theory people need to know. In fact the most impressive vision/nlp architectures are indeed uninterpretable alchemy. It would make very little difference to study it at a university. Unless of course you 're going for probability theory.
Are you defining "need to know" as in the state of the art methods of today? Problem is the next stage after mature technology is commodity. We'll be getting daily spam from India offering to fulfill all our "A.I. design" needs just as with web design. If all you have to do is follow a blog post or video on how to use a prepackaged framework to get a job done, than everyone else can do it too.
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I think its way different than that, those would just be precursors, and in cases like real analysis, superfluous. Instead it would look something like, I have a Universal Sentence Encoder architecture, but its not performing well on my data, aside from tweaking the training set, how can I take this architecture and change it to work better with my individual problem? Assume here that the problem one is trying to sol…
I believe Google's AutoML is attempting to answer these types of questions. It's obviously internal-only so others can't fork the research...but it has helped them invent new specific networks like "EfficientNet for EdgeTPU" [0]. I think humans can still invent new macro structures like CNN's...but humans are inherently shit at analyzing "what if we removed one neuron in the 2nd hidden layer?". The subtle tweaking is…