Science of Machine Learning (was Machine Learning , some thoughts)

GF gfrer at luna.nl
Sat Jun 30 12:53:07 EDT 2018


Data of perfect quality means, in my opinion, data and their complete context.
A diagnosis by a nurse is not the same as one by a patiente, or strting intern, or one MD with 20m years experience.
Just mentioning one example.



Gerard   Freriks
+31 620347088
  gfrer at luna.nl

Kattensingel  20
2801 CA Gouda
the Netherlands

> On 30 Jun 2018, at 17:16, Philippe Ameline <philippe.ameline at free.fr> wrote:
> 
> Le 27/06/2018 à 22:26, Bert Verhees a écrit :
> 
>> On 27-06-18 16:43, Philippe Ameline wrote:
>>> 1) you can find a bunch of practitioners that agree on working extra
>>> hours to comment a big bunch of images, or
>> 
>> Did I tell you about the plant-app? I believe I did. 700.000 pictures
>> are reviewed, often by volunteers.
>> 
>> The app recognizes 16000 plants. Important is how you do it, and that
>> it does not cost effort by the volunteers, for example in relation to
>> what they do anyway.
>> 
>> https://plantnet.org/ <https://plantnet.org/>
>> 
>> It is a French product.
> 
> Dear Bert,
> 
> The plant-app was the subject of your initial post.
> 
> The math in support of deep learning are being studied. To make it
> short, it remains somewhat mysterious since such classification
> algorithms "should not work", but actually, they do ;-)
> 
> From an article I just read, such NP complete algorithms are similar to
> finding a needle in a hay stack and shouldn't provide valuable
> answers... unless the conditions (large enough needle, correctly ordered
> stack) make the problem handy.
> 
> To sum it up, data quality (signal over noise ratio) is paramount. In
> the plant-app you mentioned, adding a certain level of fuzziness
> (improperly labeling images or adding images of objects that are not
> plants) could probably make the whole app plainly crappy.
> 
> Just to say that building a deep learning system starts from making
> certain that the data it will be fed with are of proper quality. This is
> usually not the case in medicine, largely because IT is considered a
> back office concept and there is seldom the kind of feedback loop that
> could lead to having errors fixed.
> 
> My point is that you can perfectly (but with considerable efforts)
> organize a trained network of practitioners to feed a "data lake" in
> order to train a neural network... but will probably be disappointed if
> you try to process existing information.
> 
> Best,
> 
> Philippe
> 

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