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DONT THREAD ON ME posted:can anyone rec a small ML project or guide for someone who doesn't want to do ML but wants to understand it a little more? 3blue1browneye has a short series https://www.youtube.com/watch?v=aircAruvnKk&list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi&index=1 this guy gives a understanding of image processing: https://adeshpande3.github.io/adeshpande3.github.io/A-Beginner%27s-Guide-To-Understanding-Convolutional-Neural-Networks/ and fastai (course 3) is a the best intro ive found. he does a top-down approach tho, so you wont learn the meat and potatos until halfway through. and youre probs looking at a good 80 hours for doing this course https://www.youtube.com/watch?v=XfoYk_Z5AkI
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May 30, 2019 18:35
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Lysidas posted:reverse the direction so instead of reducing dimensionality, it increases, like https://science.sciencemag.org/content/358/6364/793 i wanna make a robot nose that u put in ur house and uses ml to detect different smells like carbon monoxide or formaldehyde or various vocs or who just farted. and every once in a while, the model gets updated and sent to all noses so now it can know new smells. two problems tho are: 1. if they dont match spec exactly (like down to the size of each hair sensor) then theyll each need to be retrained every time a new model gets pushed. u might be able to build that training data in factory, but itll still be super annoying 2. theyll probs get clogged, so youll need it to be able to sneeze once a week to clear itself out
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May 30, 2019 21:34
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eschaton posted:I’d train a large number of smaller networks on different characteristics to recognize, and use additional separate networks to determine confidence according to recognitions, etc. finally arriving at the one confidence value Thats what resnet does, except it picks those attributes automatically and you can later verify what each output from each layer means. And what u can also do is load resnet and train the last layers with new images to detect the things u want (like different breeds of animals). Then u dont need millions of images anymore. You can get by with like 30 and it will still be super accurate for ur new specific case.
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Jun 1, 2019 14:20
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One of the most valuable parts of ml is u can take a existing model and then train it with ur data to make it do what u want. So with each new tech, u wait for the super nerds to do all the work for u, then u use their model to do ur stuff easy peasy.
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Jun 1, 2019 14:22
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worthless people tend to complain a lot
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Jun 3, 2019 23:08
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