Is it mostly sparse or non-sparse? Both of them are single-node library
so they seems not possible to use directly.
On 09/10/2014 01:29 PM, Dmitriy Lyubimov wrote:
The biggest problem today (in my opinion) is mahout-math.
(1) cost/type based optimization of matrix-matrix multiplication
(2) cost/type based optimization of elementwise matrix-matrix operations
There is already some work done there, especially in the realm of
vector-vector opreations, so matrix-matrix operations that work with
matrices backed by a set of vectors, should naturally benefit from that.
Other two noble goals have been:
(3) jBLAS backed matrices, including a part of (1) and (2)
(4) JCuda backed matrices, including as a part of (1) and (2)
Otherwise, if you are interested in writing yet-another quasi-algebraic
solver methodology, it is a second priority but would be welcome provided
you provide references to principled approach and its adaptation to scaled
operations strategy, for review, and as long as long as preferrably this
method is not yet part of MLib in spark.
-d
On Wed, Sep 10, 2014 at 10:14 AM, Ankit Sharma <[email protected]>
wrote:
Hello,
I have been an user of Mahout for quite sometime now and got really exited
when I heard mahout is moving to Spark. Today I played around with Linear
Regression example and browsed some of the spark Machine Learning(ML) code.
It was really interesting to see how intuitive the entire process is.
I have background in data science model building and I would like to
contribute in the development process. So, I would like to get some advice
on what has already been completed on ML side and from where I can start?
I have couple of ideas like I can start with either some classification
algorithm like SVM or build(enhance) some simple building blocks. You can
throw in your suggestions and I'll be see which one falls into my domain,
and try to work on them.
thanks & best regards,
Ankit Sharma
Data Science Professional
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