; strategy of how
>> you will make this work? Like slides for a design diagram maybe?
>> It might be useful for us to see how the internals would look like at our
>> end too.
>>
>> Cheers,
>> Manu
>>
>>
>>
>> --
>> View this
It might be useful for us to see how the internals would look like at our
> end too.
>
> Cheers,
> Manu
>
>
>
> --
> View this message in context:
> http://apache-flink-incubator-mailing-list-archive.1008284.n3.nabble.com/Kicking-off-the-Machine-Learning-Library-tp2
at our
end too.
Cheers,
Manu
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work.
>
> Cheers,
> Manu
>
>
>
> --
> View this message in context:
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Thanks Ted, I have reached out to them as well. The more requests the
merrier I suppose =)
- Henry
On Wed, Jan 14, 2015 at 11:45 AM, Ted Dunning wrote:
> On Thu, Jan 8, 2015 at 5:09 PM, Henry Saputra
> wrote:
>
>> I am trying to hook us up with H2O guys, lets hope it pays off =)
>>
>
> I know t
On Thu, Jan 8, 2015 at 5:09 PM, Henry Saputra
wrote:
> I am trying to hook us up with H2O guys, lets hope it pays off =)
>
I know the CEO and CTO reasonably well and will ping them.
Thank you Dmitriy!
That was quite a bit food for thought - I think I will need a bit more time
to digest that ;-)
Especially the part about the algebraic data structures and how they allow
you to pipeline and combine algorithms is exactly along the lines of what I
was thinking - thank you for shar
In terms of Mahout DSL it means implementing a bunch of physical operators
such as transpose, A'B or B'A on large row or column partitioned matrices.
Mahout optimizer takes care of simplifying algebraic expressions such as 1+
exp(drm) => drm.apply-unary(1+exp(x)) and tracking things like identical
Hi Till,
Yes, that is pretty much how they do it.
The trick is to get access to shared data between Spark realm and H2O realm.
Prev gen they use Tachyon but in the latest stint they use run H2O in
the Spark executor to use the heap shared memory to do the trick.
I am trying to hook us up with H2O
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