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Posts Tagged ‘machine learning’

Because when we sit down and think about a problem, when we take the time to not only understand what our feature space “is” and what it “implies” in the real-world — then we are acting like machine learning scientists. Otherwise, we [are] just a bunch of machine learning engineers, blindly performing black box learning and operating a set of R, MATLAB, and Python libraries.

The takeaway is this: machine learning isn’t a tool. It’s a methodology with a rational thought process that is entirely dependent on the problem we are trying to solve.

Get off the deep learning bandwagon and get some perspective – PyImageSearch

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Ever itched to do machine learning and data mining on streams? On huge, big data streams?

We have a solution for you!

SAMOA (Scalable Advanced Massive Online Analysis) is a platform for mining big data streams. It features a pluggable architecture that allows it to run on several distributed stream processing engines such as Storm and S4. SAMOA includes distributed algorithms for the most common machine learning tasks such as classification and clustering. For a simple analogy, you can think of SAMOA as Mahout for streaming.

SAMOA is currently in Alpha stage, and is developed in Yahoo Labs in Barcelona. It is released under an Apache Software License v2.

Thanks to everybody who made this release possible!

read more here on Yahoo engineering

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Person that does not know how machine learning works,
but knows what to do when machine learning does not work.

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Finally over, the AI and ML Stanford classes finished last week.

The courses were a really interesting experience, and I think I learned a lot from them.
Apart from the technical content, which is very useful, I learned things about myself.
For instance, how long I can stick and persevere, how much motivation plays an important role in what I do, how well I can manage my time and how much I can push my limits.

And now, because I am proud of myself:
<boast>
I managed to finish the advanced track in both classes and ended in the top quartile of the final ranking for the AI class!
The ML class had no ranking.
</boast>

Next to come: NLP and PGM, Models and Algorithms.

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Data Intensive Scalable Computing
DISC=Data Intensive Scalable Computing

ML=Machine Learning
DM=Data Mining
IR=Information Retrieval
DS=Distributed Systems
DB=Data Bases

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