UPDATE: I've moved on from Numenta and would like to rewrite this post to be both objective and more current. In the meantime check out a recent po...
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Nice post - I'd completely like to second the approaches above.
I've hired people over the past few years without PhDs because they've got demonstrable results in kaggle or other ML projects. It's hard finding a recruitment agent that understands that good candidates are more than just a set of qualifications, but don't let that put you off.
Hi all,
Update! I've moved on from Numenta and would like to rewrite this post to be both objective and more current. In the meantime check out a recent podcast I did "Python for AI research".
Hey Alex, can the part about Python being the best language for ML. Is it purely a community thing or is there something about the language itself?
Hey Ben,
Both -- it's mostly about the strong, growing community, but that derives from Python being a versatile language. Python is relatively easy to learn and use, can be quick and dirty, fast enough for most applications*, and has a ton of quality libraries, making it a great ecosystem for ML research.
It's worth noting that most ML researchers come out of academic labs where solid SW engineering isn't required, so Python is an obvious choice (over e.g. C++) for ML researchers in academia and industry. As a result we see main ML libraries and open-source projects written in Python -- TensorFlow, Keras, scikit-learn, etc. -- which further proliferates the adoption and use of Python for ML.
*After prototyping and optimizing an algorithm in Python, we may implement it in C++ for the speedups.
Cheers,
Alex