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Stephan Gouws
Works at Google
Attends Stellenbosch University
Lives in San Francisco
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Stephan Gouws

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We are open-sourcing our internal deep learning framework!
 
Open Source Release of TensorFlow

I'm very excited to announce the open source release of the TensorFlow machine learning library that I and many others at Google have been building.

This blog post gives an overview:
  http://googleresearch.blogspot.com/2015/11/tensorflow-googles-latest-machine_9.html

The web site has a number of tutorials and documentation about the system:
  http://tensorflow.org

The source is on GitHub:
  https://github.com/tensorflow/tensorflow

You might also be interested in the whitepaper we've prepared that describes TensorFlow in more detail:
  http://tensorflow.org/whitepaper2015.pdf

We'd love to hear what people think.
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Stephan Gouws

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Nice talk on the potential alienating effect of information personalisation by sites like Facebook and Google. I think what he is not considering is the normalising effect of social sharing of content: What was blocked to me was seen by a friend and retweeted, plus-oned or posted on his Facebook, thereby at least mitigating the "filter bubble" effect. http://www.ted.com/talks/eli_pariser_beware_online_filter_bubbles.html
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Stephan Gouws

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LOL :D
Bradley Wells originally shared:
 
whos circle am I in?
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Stephan Gouws

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As a novice when it comes to quantum computing, I really like how +Hartmut Neven  is able to convey these ideas using simple analogies without trivializing the content too much, or patronizing the reader. I just like his writing style in general.
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I've shared this before, but this is worth reading again: Ariah Haghighi writes that most interesting real-world problems are not simply solved by thowing an off the shelf machine learning solution at it; knowledgeable insight into the problem, allowing us to decompose the problem space into several smaller, more manageable sub-problems (possibly using simpler ML techniques to solve each sub-problem) is the way to go. Conversely, most significant performance improvements are usually the result of this process, rather than finding "the ultimate" one-size fits all ML solution. http://radar.oreilly.com/2012/04/great-machine-learning-products.html
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Doesn't explicitly deal with problem solving techniques such as divide and conquer, but has some interesting general thoughts on using ML for problem solving
http://videolectures.net/nipsworkshops2011_williamson_machine/
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Have him in circles
244 people
Nadia slam's profile photo
brother man's profile photo
Community's profile photo
KyungHyun Cho's profile photo
Herman Maritz's profile photo
Innes Linder's profile photo
Luwalaga John Groover's profile photo
bi kini's profile photo
Rudra Poudel's profile photo
Education
  • Stellenbosch University
    PhD (Machine Learning/NLP), 2011 - present
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  • Google
    SWE Intern, 2014 - present
  • Stellenbosch University
    PhD student in Machine Learning/NLP, 2010 - present
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