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Using Google Cloud AutoML to Classify Poisonous Australian Spiders

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Re: Using Google Cloud AutoML to Classify Poisonous Australian Spiders

#21

Post by synaesthesisx »

sjg007 wrote: post #9
thomasfoster96 wrote: post #8

Earlier quoted context omitted.

That’s probably because it’s fairly widely known (in Australia) that if you’re bitten by a red-back or funnel-web spider that you seek medical attention, as you would for a venomous snake. A quickly treated bite is almost never fatal. The app helps people make the decision as to whether its a harmless spider or something that requires urgent medical attention.

I think if you are bit you should seek medical attention regardless of what an app says.

Ah, unless you live in the United States - then it becomes a question of whether it's worth it.

Re: Using Google Cloud AutoML to Classify Poisonous Australian Spiders

#26

Post by zaarn »

thrownaway954 wrote: post #4
Zhenya wrote: post #2

Something like this could actually save lives. Take a photo of what bit you, then the app could provide an answer (with a confidence level) of "do I need to go to the hospital" and provide a few example apps for various matches with level of danger. Thanks for sharing!

it's Australia... the answer is always "YES!!!"

The app could probably be optimized to the following:

    def spiderIsDeath():
      return True
Though surprisingly Australia does host some non-lethal animals and insects, including spiders. Most of the time people will be fine when bitten by spiders in Australia, treatment is quick in almost all cases.

Re: Using Google Cloud AutoML to Classify Poisonous Australian Spiders

#27

Post by nl »

gajju3588 wrote: post #25

Auto labeling would be way forward for Supervised Algorithms. Get some data to annotate from your team, and tag rest of them using auto-labeling. https://dataturks.com/ could be such player, Not sure how will these survive in front of Google.

There's no such thing as auto-labeling.

Data Turks is manual labeling.

There is active learning[1] and related algorithms where you trace the boundary of your classifier and pass examples along that boundary to be manually labeled (as they are the ones the classifier is most unsure about).

But there is nothing "auto" about this - it's just being smart about where to deploy the manual labor.

[1] https://en.wikipedia.org/wiki/Active_learning_(machine_learn...

Re: Using Google Cloud AutoML to Classify Poisonous Australian Spiders

#28

Post by nl »

michaelgreen wrote: post #3

This is huge, and it's only an alpha. I begun reading about AutoML/Neural Architecture searches around ~year ago and something I've been thinking about is: Why doesn't this just move the optimization problem? Aren't you now just optimizing your DeepRL network rather than the network you're trying to optimize?

The idea of AutoML (in this case[1]) is to improve the NN architecture for a given type of problem.

In "normal" machine learning this is basically hyperparmater optimization for a given dataset (eg, the depth of a random forest, XGB parameters, the best random seed/jk )

In this case is tests different combinations of operators on a known dataset to see what performs the best. So it is optimizing the prediction network

(Also this isn't DeepRL, it's a deep neural network. I think that was a typo)

[1] https://research.googleblog.com/2017/05/using-machine-learni...

Re: Using Google Cloud AutoML to Classify Poisonous Australian Spiders

#29

Post by gajju3588 »

nl wrote: post #27
gajju3588 wrote: post #25

Auto labeling would be way forward for Supervised Algorithms. Get some data to annotate from your team, and tag rest of them using auto-labeling. https://dataturks.com/ could be such player, Not sure how will these survive in front of Google.

There's no such thing as auto-labeling. Data Turks is manual labeling. There is active learning[1] and related algorithms where you trace the boundary of your classifier and pass examples along that boundary to be manually labeled (as they are the ones the classifier is most unsure about). But there is nothing "auto" about this - it's just being smart about where to deploy the manual labor. [1] https://en.wikipedia.o…

Lets say we want to create a labeled data for text summarization for medium articles. Could the highlighted part be used as summary, its not auto labeled per se, but can be a proxy and passed to labelers to verify/edit.

Re: Using Google Cloud AutoML to Classify Poisonous Australian Spiders

#30

Post by nl »

juskrey wrote: post #20

Isn't that something available in, say, Mathematica in 3 lines of code? https://wolfram.com/language/11/image-and-signal-processing/...

No.

The example there is 7 lines (counting the NN description as one line). That's using a (easy) pre-existing dataset too, and a primative neural network.

That's roughly the same as in Python using something like the fast.ai library. I think that comes for 4 lines (not including data wrangling or inputs):

data = ImageClassifierData.from_paths(PATH, tfms=tfms_from_model(arch, sz)) learn = ConvLearner.pretrained(arch, data, precompute=True) learn.fit(0.01, 2) log_preds = learn.predict()

See [1]

Also note that this AutoML version uses zero lines of code.

[1] https://github.com/fastai/fastai/blob/master/courses/dl1/les...

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