A Gentle Introduction to Bayes’ Theorem for Machine Learning
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A Gentle Introduction to Bayes’ Theorem for Machine Learning
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Re: A Gentle Introduction to Bayes’ Theorem for Machine Learning
#2Always nice to see a little piece of python code show up whenever one of these ML blogs get posted to HN. It's becoming, or has become, the lingua franca of this field.
Re: A Gentle Introduction to Bayes’ Theorem for Machine Learning
#3This is a far gentler introduction, and the rest of the blog is pretty good too:
https://www.countbayesie.com/blog/2016/5/1/a-guide-to-bayesi...
Edit: this is a different take on the subject but an enjoyable and accessible read too: http://mbmlbook.com/toc.html
Re: A Gentle Introduction to Bayes’ Theorem for Machine Learning
#4change my mind: bayes in practice is just a way to regularize your model and the language of bayes makes it seem principled but really you could use literally any regularizer and it would work almost just as well. i believe this because ultimately you're always going to minimize loglikelihood anyway (and so the prior becomes the regularization term).
Re: A Gentle Introduction to Bayes’ Theorem for Machine Learning
#5I remember the first time I heard of Bayes Theorem , it took me a while to grasp it.
Re: A Gentle Introduction to Bayes’ Theorem for Machine Learning
#6I appreciate the lack of math notation, for many with a poor mathematics backgrounds it feels like a huge wall into getting into interesting and useful theories.
Re: A Gentle Introduction to Bayes’ Theorem for Machine Learning
#7change my mind: bayes in practice is just a way to regularize your model and the language of bayes makes it seem principled but really you could use literally any regularizer and it would work almost just as well. i believe this because ultimately you're always going to minimize loglikelihood anyway (and so the prior becomes the regularization term).
Counterpoint: regularization is just a way of specifying a Bayesian prior for maximum a posteriori estimation.
Re: A Gentle Introduction to Bayes’ Theorem for Machine Learning
#8change my mind: bayes in practice is just a way to regularize your model and the language of bayes makes it seem principled but really you could use literally any regularizer and it would work almost just as well. i believe this because ultimately you're always going to minimize loglikelihood anyway (and so the prior becomes the regularization term).
Well you could draw samples of the parameters with a Bayesian setup via MCMC or get a distribution over them via a variational approximation, rather than getting some sort of maximum likelihood (MAP whatever) value for the parameters of the model via solving an optimization problem. This seems much more general (and practically useful). So I think it is the other way around regularizers are just priors (that you arrived at somehow).
Re: A Gentle Introduction to Bayes’ Theorem for Machine Learning
#9Always nice to see a little piece of python code show up whenever one of these ML blogs get posted to HN. It's becoming, or has become, the lingua franca of this field.
I'm very excited for Tensorflow for Swift, personally. The type system will make development so much more pleasant IMO. Judging by the way things have been lately though, I imagine that by the time TF for Swift is ready I'll be reaching for PyTorch at every opportunity anyways...
Re: A Gentle Introduction to Bayes’ Theorem for Machine Learning
#10Always nice to see a little piece of python code show up whenever one of these ML blogs get posted to HN. It's becoming, or has become, the lingua franca of this field.
I'm very excited for Tensorflow for Swift, personally. The type system will make development so much more pleasant IMO. Judging by the way things have been lately though, I imagine that by the time TF for Swift is ready I'll be reaching for PyTorch at every opportunity anyways...
I think the fact that Swift has first-class automatic differentiation is an even bigger deal than the fact that it is strongly-typed. Here is an interesting write-up about it...
https://gist.github.com/rxwei/30ba75ce092ab3b0dce4bde1fc2c9f...