
Identifying dog breeds using Keras - rjtrickett
https://able.bio/devforfu/identifying-dog-breeds-using-keras--767qpxs
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ageitgey
Machine learning is still considered "new" or "difficult" so people often
respond to posts like this with comments like the one that says "Nothing
impressive, existing architectures were used". Writers only get enthusiasm if
they raise a benchmark with a brand new approach.

For researchers and people spend a lot of time on ML, I totally get that.
There isn't anything _new_ here. In fact, I correctly guessed nearly the
entire content of the article from the title alone (transfer learning, using
Keras' "Application" models, etc). But why does that matter? It's still a
useful article to a lot of people and it's well written.

There is still a dearth of practical advice on how non-specialist developers
can solve everyday problems with ML. The truth is that the majority of real
projects that real companies need done could be done exactly this way. Using
this approach, a developer could get decent results and be done with a project
in hours instead of months.

For some reason everyone thinks it's fine to post an article about how to
speed up your CRUD website with a Postgres indexes even though it's been done
millions of times before, but ML posts get dismissed if they aren't making
some kind of research breakthrough. I personally feel like we need a lot more
approachable discussion around ML like this in the world. Every dev should
feel comfortable getting started with this kind of work if they want to.

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bcheung
Agreed. There's too much of an academic paper culture around ML. It makes the
field very unapproachable. It's as if people don't actually use it for
anything practical and they only care about the science and math.

It is getting better though. It's great to see people post articles like this
and even if it has been done before seeing different approaches and seeing it
written in different ways goes a long ways towards educating people.

I'm hoping the JavaScript ML / DL ecosystem expands more since that would
greatly accelerate its adoption and allow more developers access to ML.

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riku_iki
Recent tendency in ML/DL academic papers is applying described approach on
some public benchmark, and showing improvement over current State of The Art
results.

~~~
bcheung
ML/DL is certainly doing better than most but it still has the academic
culture. It is increasingly more common to release code on github so the
result can be reproduced and people can adapt their research.

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dna_polymerase
While it might not be novelty (and therefor it doesn't seem impressive to
some), Transfer Learning is actually highly efficient even for small datasets
and should at least be considered before wasting energy and money in building
your own convnet. Its a good first step when tackling a new problem in this
problem space.

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bcheung
Interesting. I did the same exercise with the pretrained Xception and 2 dense
layers at the end and only got 83% accuracy. Thanks for sharing the tweaks you
made, that's good to know. I wasn't aware of those techniques.

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khawkins
I'd love to see this network tested on hybrid breeds (but still trained on
pure breeds) to see if the probabilistic output would be divided between the
two parent breeds.

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curiousgal
Nothing impressive, existing architectures were used, InceptionV3, Xception
and InceptionResNetV2. Fine-tuning those models resulted in a slightly worse
accuracy.

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eftychis
Still it seems like a good write up. Yes, it is not innovative, it provides a
good presentation and "here's how I solved this every day, company problem." I
think this has a benefit in the Data science. Yes, it glosses over the
retrieving and preparing your data section, which I feel is the most work in
these problems. However, it gives an idea of how to combine your knowledge and
think as a new Data Scientist. So I think it is helpful to some people. As
other people said there is a plethora of similar articles in other Software
Engineering fields that rise consistently in the front page of Hackernews.

