
Core ML Community Tools - gok
https://github.com/apple/coremltools
======
Erikun
There's a wwdc presentation on this where they show the tools in action. The
first part is Core ML and the Tools part starts at 21 minutes in (it is about
40 minutes in total so definitely worth watching I found).
[https://developer.apple.com/videos/play/wwdc2017/710/](https://developer.apple.com/videos/play/wwdc2017/710/)

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skierscott
Turns out generating a ML model is pretty easy too, even after training in
Python. There's a simple 2 or 3 line conversion from keras/sklearn to an ML
model.

[http://stsievert.com/blog/2017/06/11/coreml/](http://stsievert.com/blog/2017/06/11/coreml/)

I verified this because I'm a graduate student studying ML and I'll likely use
this.

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melling
Any good Core ML blogs that are not already from here?

[http://www.h4labs.com/dev/ios/swift.html?q=core+ml&age=10000](http://www.h4labs.com/dev/ios/swift.html?q=core+ml&age=10000)

~~~
saimiam
I'm curious to learn how to think about the applicability of ML inside my app
or server. I know I could hire an ML professional to do the thinking for me
but a bootstartup can't really afford the time, money, and salesmanship
required to locate and hire such a person.

What I'd love to see is something which says "hey! Here's ML technique X and
these are the areas where X or something similar to X can be used."

Schools do well in transmitting this information to us - "here's
multiplication. Use it to figure out how much 5 boxes of cereal cost if one
costs $4.71" and so on.

If companies are trying to mass-ify ML, they need to de-ivory-tower-ify the
applicability of ML in everyday thinking too.

Take face recognition - easy as pie to try out and understand but apart from
the extremely limited use case of finding friends to tag in social media, what
else can it be used for? Can it used as a diagnostic tool in neurology or
ophthalmology? Can face recognition be used in police sketchups? No idea and
not many blogs exist to think about such things.

The other problem with ML is that it has no component parts that we can
extract and use on its own. Every ML technique comes fully formed - image
recognition is a complete API. Are there constituent parts inside image
recognition that, when combined with constituent parts of (say) face
recognition, become a better ML tool? Again, I have no idea and no blogs
discuss this either.

I want to use ML.

(Also, if you're going to link to your own blog, you should mention that
maybe? Your HN username and your blog name are disjoint enough for the link
not to be obvious.)

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mendeza
Maybe I can assist? I took a graduate course at Cornell called Applied Machine
Learning[1] last year. The lecture notes are really great. The lecture notes
survey a huge list of machine learning algorithms, and highlights its useful
applications.

If you don't have understanding of regression vs classification, maybe skim
through online resources to get a high level understanding of machine
learning. Then you can dive into the lecture notes where you get breadth and a
little depth.

Also the book, Building Machine Learning Systems with Python [2], is an
amazing book where it applies machine learning techniques with python. I think
this is the best resource on how to begin applying machine learning methods
and it was helpful when I was implementing algorithms for the class, like kNN
for clustering and PCA reduction + log regression for face recognition.

[1]
[https://cornelltech.github.io/cs5785-fall-2017/lectures.html](https://cornelltech.github.io/cs5785-fall-2017/lectures.html)

[2]
[http://totoharyanto.staff.ipb.ac.id/files/2012/10/Building-M...](http://totoharyanto.staff.ipb.ac.id/files/2012/10/Building-
Machine-Learning-Systems-with-Python-Richert-Coelho.pdf)

~~~
saimiam
Thank you. Will review.

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cantagi
Glad Apple have finally put coremltools on Github. It looks like they've added
a lot of stuff recently as well.

