[mlpack] mlpack 2.0.0 released

[Jaskaran Singh]

Congrats everybody and merry christmas.

On 25 December 2015 at 05:10, Ajinkya Kale <kaleajinkya at gmail.com> wrote:

> Congratulations all on 2.0.0 release!
> Do we have a page on who and where all is mlpack used ?
>
> On Thu, Dec 24, 2015, 08:20 Ryan Curtin <ryan at ratml.org> wrote:
>
>> Hello there,
>>
>> This has been a long time coming...
>>
>> Last night I tagged mlpack-2.0.0 and uploaded it to the mlpack website.
>> You can get it here:
>>
>>   http://www.mlpack.org/files/mlpack-2.0.0.tar.gz
>>
>> There has been a significant amount of refactoring and hard work by lots
>> of people since the last release in January, and the changelog is fairly
>> long, so I'll put what I think are the most exciting bits below:
>>
>>  * Parallelization: the DET (density estimation trees) code is now
>>    parallelized with OpenMP.  As time goes on, parallelization will be
>>    added to other algorithms, but note that you can also use Armadillo
>>    with OpenBLAS, which will parallelize all the linear algebra calls.
>>
>>  * Model saving and loading: where appropriate, all of the command-line
>>    programs now support loading and saving models.  So you can train,
>>    say, a logistic regression model, and save it for later use.  This is
>>    also possible with techniques like all-k-nearest-neighbor search,
>>    which allow you to save the tree built on the points.  Model
>>    serialization support is also available from C++, too, of course.
>>
>>  * Significant refactoring: most machine learning algorithms now follow
>>    the same API, and documentation has been improved.
>>
>>  * Tree-based algorithms now support multiple types of trees in a far
>>    easier manner.
>>
>>  * The k-means code now supports five different algorithms, many of them
>>    far faster than the original implementation.
>>
>>  * Add streaming decision trees (Hoeffding trees) for fast classifiers
>>    on huge datasets.  This supports both categorical and numeric
>>    features.
>>
>>  * No more dependence on libxml2; boost::serialization is used instead.
>>
>>  * Armadillo minimum version bump to 4.100.0.
>>
>>  * All mlpack programs are now prefixed with 'mlpack_', so for instance
>>    'allknn' is now 'mlpack_allknn'.
>>
>> Also exciting, in my opinion, is the community that has grown around
>> mlpack.  Here are some neat and interesting statistics:
>>
>>  * mlpack has almost 40 contributors
>>
>>  * mlpack has now been downloaded at least 35k+ times (my logs
>>    undercount)
>>
>>  * mlpack has been used in at least 40 academic papers (also a lowball
>>    estimate)---and this number is increasing faster and faster
>>
>>  * the mlpack codebase now contains about 60k source lines of code
>>    (SLOC)
>>
>> So I have to say, I'm very happy that we have built tools that people
>> are finding useful!  I hope that this trend continues. :)
>>
>> For the full changelog in mlpack-2.0.0, see
>> http://www.mlpack.org/history.html.  Over the next few days/weeks,
>> updated mlpack packages will be pushed to the package repositories of
>> various distributions.
>>
>> Lastly, some notes about the future.  Upcoming releases will follow the
>> versioning guidelines now present in UPDATING.txt (semantic versioning):
>> https://github.com/mlpack/mlpack/blob/master/UPDATING.txt
>>
>> Future goals include a flexible framework for artificial neural networks
>> (prototype code can currently be found in the master branch in
>> src/mlpack/methods/ann), generic bindings to other languages such as
>> Python, Java, MATLAB, and others, parallelization support for more
>> algorithms via OpenMP, a new implementation of random forests, and
>> dimensionality reduction or manifold learning techniques.
>>
>> I'm also hopeful that we can have a much more frequent release cycle,
>> more like once a month or more, following the versioning guidelines I
>> mentioned earlier.
>>
>> So, I hope that you find this release useful!  Please feel free to
>> report any bugs as Github issues to https://github.com/mlpack/mlpack or
>> to this mailing list, or to the #mlpack channel in freenode.
>>
>> --
>> Ryan Curtin    | "Good Lord - I've heard about this - cat juggling!
>> ryan at ratml.org | Stop! Stop! Stop it!" - Navin R. Johnson
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>>
>
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>


-- 
Cheers
*Jaskaran Singh Virdi*
*Software Development Engineer*
*Zomato*
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