ActionML

[ActionML @actionml]

Come to the online (and free) Apachecon and watch us talk about the core of the Universal Recommender -- the Correlated Cross-Occurrence (CCO) algorithm, implemented in Apache Mahout.

https://t.co/GCdbVX9KWj

[ActionML @actionml]

Apachecon goes virtual and the Universal Recommender will be there. Check out the Mahout track: https://t.co/dyLWRR1IhL

[ActionML @actionml]

Use of Deep Learning is becoming very difficult if you don't have huge data. We've said it before. Here is a way to extract features from giant models that work with traditional ML. I think of is a condensing the information into more easily digests bits. Great read.

[occam @occam]

Now a good explanation for the "never enough data to make Deep Learning perform" effect and how to turn NNs trained on truly huge data into feature extractors for use with more traditional ML. Highly recommended article! https://t.co/VIBJr8R1KK

[ActionML @actionml]

The Universal Recommender v0.7.3 released. Now with cross-recommendations; "viewed this bought that" Use it to help a user find the target of a browsing or search session. https://t.co/E3aujqsP6G

[ActionML @actionml]

PredictionIO graduates to a top level project at Apache. The Mahout based Universal Recommender runs on it. https://t.co/JL5Q9pz8So

[ActionML @actionml]

A personal story of obsession and the gravitational attraction of Apache thru Mahout and PredictionIO https://t.co/P5WV8GxOpf

[Gerard Maas @maasg]

Modern microservices architectures are scaled-up versions of the actor model.

[ActionML @actionml]

#apachemahout now runs on GPUs, this is one example of what is next. https://t.co/IYklpvhsDX via @ExtremeTech

[occam @occam]

Commodity clusters may give way to massively parallel multi-core chips and speculative execution
https://t.co/sj2OFha16R via @physorg_com

[ActionML @actionml]

Multi-domain predictive AI or how to make one thing predict another Mahout's CCO in a Recommender https://t.co/7YRQoogsVY via developerworks

[Lisa Seacat DeLuca @LisaSeacat]

What will the dressing rooms of the #future look like? https://t.co/ABaDiMS65z #nearFuture @ApacheMahout #IoT @developerWorks

[ActionML @actionml]

IBM's Watson may get most of the headlines but Ted talkster Lisa Seacat calls out the algorithm inside the Universal Recommender here. https://t.co/x7bjdrrRsX

[occam @occam]

Ted talker Lisa Seacat talks about Apache Mahout and CCO's mashup with the IoT in next gen commerce. https://t.co/BXb07E7Zyv

[MLconf @MLconf]

Great talk by Andrew Musselman! @mlconfsea closing out the first half!

[ActionML @actionml]

The Universal Recommender and the landscape of PredictivAI with the CCO algoithm at the GTC GPU conference. #GTC17 https://t.co/7sNA4bNhAo

[सुनील मार्ती @suneelmarthi]

Presenting Multi-modal recommenders on GPU @occam #GTC17 @ApacheMahout

[occam @occam]

The Universal Recommender on Spark GPU clusters https://t.co/H6YOg2V4qa

[ActionML @actionml]

@lawouach Thank you for catching it. We are on it.

[Apache Incubator @ASFIncubator]

Meet PredictionIO, now in development @ the Apache Incubator
https://t.co/IAwRQunKg0 #Apache #OpenSource #MachineLearning #server @ApachePIO

[clive boulton @iC]

@occam engages #SeaScale w/ how to scale recommender systems with low latency frugally (don't put Spark in the query loop, only in model)

[ActionML @actionml]

ActionML's Universal Recommender at scale https://t.co/rfDzNMCzme

[Apache Mahout @ApacheMahout]

Come out to see @occam talk about scaling recommenders in production tonight! https://t.co/fF7spN0SRI

[occam @occam]

Describes part of the Universal Recommender with PredictionIO: https://t.co/0VVKmtr8AY https://t.co/BNlcHEZfJS