Github user debasish83 commented on the pull request:
https://github.com/apache/spark/pull/1290#issuecomment-62247730
Our sparse data very often has ~10M users and 1M features...for 1 hidden
layer net with 10K nodes, we need 10M x 10K + 1M x 10K doubles...if the model
is not distributed (like ALS design) it's not possible to put such complicated
model into memory of one node...having said that it makes sense to have a
baseline implementation so that it can be used as a reference for further
enhancements...also as a auto encoder, neural nets should generate better
results than sparse coding (which we can do in mllib through the PR) due to the
non-linearity of hidden units...
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