What I was really hoping for was a layman's translation of the maths on wikipedia [ie. how to implement]. But this is a good jumping off point for figuring out which black box to use.
I'm just saying it takes about 30 seconds to explain the Metropolis-Hastings algorithm in plain english, and the wikipedia article is almost intentionally esoteric on the matter:
Wow, this is an awesome read. I especially like how each algorithm starts with a simple explanation and then dives deeper into each lesser known vocabulary.
I'll add my noise to the cacophony and agree that this is a grat article. I've been struggling with an idea for a while, and this has opened up a whole new horizon of possibility for me. So thanks.
I'd be interested in just about every algorithm explained this way! And as a side note some of these algorithms certainly are used in computer vision, k-means for example in clustering/segmentation.
Can we have more things like this, please? This is an AMAZING introduction to these concepts.
I wish more people on Hacker News would make such clear blog posts for their personal share of cryptic knowledge, helping other people through the door.
I also want that, the side-by-side comparison specially. The follow up discussions are also helpful, someone mentioned about C5.0 I didn't know about. There are lot of algorithms/steps pieces that are needed when solving the machine learning puzzle. The article was very helpful.
In kernel density estimation, each observed data point is spread out by a "kernel" function such as a Gaussian, essentially exp(-0.5*x^2), or uniform function, f(x) = 1 for |x| < 0.5. So I think of a "kernel" as a function used to distribute a point mass.
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[ 4.6 ms ] story [ 60.8 ms ] threadWhat I was really hoping for was a layman's translation of the maths on wikipedia [ie. how to implement]. But this is a good jumping off point for figuring out which black box to use.
http://en.wikipedia.org/wiki/Metropolis–Hastings_algorithm
Meanwhile the much more complex Firefly algorithm is adequately explained in just 3 sentences that are obvious to understand:
http://en.wikipedia.org/wiki/Firefly_algorithm
https://courses.cs.washington.edu/courses/cse576/12sp/notes/...
I wish more people on Hacker News would make such clear blog posts for their personal share of cryptic knowledge, helping other people through the door.
Instead I would suggest: Logistic Regression, RandomForest and Neural Networks.
[1] https://github.com/scikit-learn/scikit-learn/blob/master/skl...
I only wish it had a little about Neural Networks and Deep Learning.