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Mathieu Blondel

Personal blog · mblondel.org/journalaiprogrammingmath

Mathieu Blondel's personal blog at mblondel.org covers ai, programming and math. Since 2011, 3 different Hacker News readers have linked it 4 times. No feed was found, so read it on the site itself.

No feed foundVisit the blog
4mentions?
3HN people?
2011–2013first – last linked?
Links on Hacker News per year · peak 2 in 2012

Too few sampled comments (3) to say how often HN recommends it.?

Why HN links it

HN readers link Mathieu Blondel's machine learning journal for practical explanations with code, such as kernel perceptron and SVM posts and a cvxopt example. Comments cite it as a learning resource.

Summarised by Claude from a sample of 3 HN comments that link here. Each comment is marked recommending, critical or neutral (citing without judging); how this works.

Mathieu Blondel, research scientist at Google DeepMind in Paris. Publications, talks, teaching material and software on machine learning, differentiable programming and optimization.

What Hacker News says?

  1. ogrisel · 2012-04-16Recommends
    Thanks for the reply. I was told by twitter that this is similar to the kernel perceptron which I don't know well either. There is a good introduction with python code snippet here: http://www.mblondel.org/journal/2010/10/31/kernel-perceptron... However it seems that you need to compute the kernel expansion on the full set of samples (or maybe just the accumulated past samples?): this does not sound very online to m…

    Comment linking /journal/2010/10/31/kernel-perceptron-in-python/

    on “What it takes to build great machine learning products ”
  2. Submitted Transparent system-wide proxy as a story · 1 points

    HN discussion
  3. Other blogs by sklearners: http://peekaboo-vision.blogspot.fr/ http://www.mblondel.org/journal/category/machine-learning/ http://gael-varoquaux.info/blog/?cat=20 http://fseoane.net/blog/ http://blog.vene.ro/

    Comment linking /journal/category/machine-learning/

    on “Why use SVM”
  4. The only tricky part would be writing a quadratic solver. Alternatives: either solve a linear SVM using gradient descent (simpler to write), or offload the core of the algorithm to an existing solver like cvxopt. edit: For an example of using cvxopt, check out http://www.mblondel.org/journal/2010/09/19/support-vector-ma...

    Comment linking /journal/2010/09/19/support-vector-machines-in-python/

    on “Machine Learning Library for C++”

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