
TensorFlow Implementation of Deep Convolutional Generative Adversarial Networks - carpedm20
https://github.com/carpedm20/DCGAN-tensorflow
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zappo2938
I have an idea for anyone who is interest in getting their hands dirty with
TensorFlow.

There is a very large conservation group in Fort Lauderdale that works with
Sea Turtles.[1] Thousands of turtles are born on Fort Lauderdale beach every
year, however, there is a problem because once hatched they move towards a
light source. So they are crossing the road towards hotels with bright lights
and street lamps at night instead of crawling into the ocean. What the
volunteers are doing is collecting massive quantities of data to show how
devastating artificial lights are to the baby turtles.

They have a massive quantity of data and they have a very good idea of when
the eggs will hatch. They go out on the beach when the eggs hatch and pretty
much make sure all the turtles make it to the ocean. Great program. They use
data like air temperature and amount of daylight to try and figure out when
the eggs will hatch so they are ready to usher them into the ocean.

They have years of data. They know the data is linked to hatching times. But,
they don't have sophisticated models.

If someone wanted an interesting project to start using TensorFlow with I
would suggest getting in touch with S.T.O.P. and request their data sets so
see if a prediction model can be developed for when the eggs hatch. Being able
to know when the eggs hatch would help the scores of volunteers who are out on
the beach protecting sea turtles.

[1] [http://seaturtleop.com/](http://seaturtleop.com/)

~~~
argonaut
I understand that it's difficult for people not experienced with machine
learning to tell what problems are actually suited for what models, but using
deep learning / Tensorflow for this is like using Hadoop to sort a 10 megabyte
file of numbers. Or writing a new Ruby on Rails app and deploying to AWS in
order to create a survey for a class project.

Plain ol' statistics, plotting, and some hand rolled features (this is
bascially "data science") is probably the best fit for this problem,
especially since it doesn't seem to me like this would be a massive dataset.

~~~
ska
This is the big problem when an technique breaks through to name recognition
in mass media. The last big one was SVM/kernel methods but it wasn't this bad.
Now we have lots of people running around wanting to hit things with the "Deep
Learning" hammer, with no real idea where it is appropriate (or worse,
offering to do it for your company without much better understanding)

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midko
I can't recommend this video because I haven't watched it yet but looking at
the slides it looks like a good survey talk about generative models and an
intro to DCGAN
[https://www.youtube.com/watch?v=KeJINHjyzOU](https://www.youtube.com/watch?v=KeJINHjyzOU)

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gnarbarian
They all look the same to me.

<kidding>

Love to see with with more training data.

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platz
> "Deep Convolutional Generative Adversarial Networks"

Only 4 qualifiers? We can do better.. 5, 6 even 7 are now on the horizon!

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ruraljuror
_ "

