Practical Probabilistic Programming

What Hacker News says

1. flor1sNov 2017
   Avi Pfeffer's "Practical Probabilistic Programming" (Manning) is a nice book to learn about the concepts. It uses his own Scala based framework Figaro.
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2. flor1sOct 2017
   A nice beginner friendly book about Probabilistic Programming is the book by Avi Pfeffer: "Practical Probabilistic Programming" (published by Manning). The only downside of the book is that it used Pfeffer's own Scala library called Figaro, which does not seem to get as much attention as projects such as Stan and Edward.
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3. adamnemecekNov 2016
   To be honest, this isn't the best list, it's a bit too blog heavy. I've started reading up on ML only recently but here are my recommendations. Note that I haven't went through all of them in entirety but they all seem useful. Note that a lot of them overlap to a large degree and that this list is more of a "choose your own adventure" than "you have to read all of these".

   Reqs:

   * Metacademy (http://metacademy.org) If you just want to check out what ML is about this is the best site.

   * Better Explained (https://betterexplained.com/) if you need to brush up on some of the math

   * Introduction to Probability (https://smile.amazon.com/Introduction-Probability-Chapman-St...)

   * Stanford EE263: Introduction to Linear Dynamical Systems (http://ee263.stanford.edu/)

   Beginner:

   * Andrew Ng's class (http://cs229.stanford.edu)

   * Python Machine Learning (https://smile.amazon.com/Python-Machine-Learning-Sebastian-R...)

   * An Introduction to Statistical Learning (https://smile.amazon.com/Introduction-Statistical-Learning-A...)

   Intermediate:

   * Pattern Recognition and Machine Learning (https://smile.amazon.com/Pattern-Recognition-Learning-Inform...)

   * Machine Learning: A Probabilistic Perspective (https://smile.amazon.com/Machine-Learning-Probabilistic-Pers...)

   * All of Statistics: A Concise Course in Statistical Inference (https://smile.amazon.com/gp/product/0387402721/)

   * Elements of Statistical Learning: Data Mining, Inference, and Prediction (https://smile.amazon.com/gp/product/0387848576(

   * Stanford CS131 Computer vision (http://vision.stanford.edu/teaching/cs131_fall1617/)

   * Stanford CS231n Convolutional Neural Networks for Visual Recognition (http://cs231n.github.io/)

   * Convex Optimization (https://smile.amazon.com/Convex-Optimization-Stephen-Boyd/dp...)

   * Deep Learning (http://www.deeplearningbook.org/ or https://smile.amazon.com/Deep-Learning-Adaptive-Computation-...)

   * Neural Networks and Deep Learning (http://neuralnetworksanddeeplearning.com/)

   Advanced:

   * Probabilistic Graphical Models: Principles and Techniques (https://smile.amazon.com/Probabilistic-Graphical-Models-Prin...)

   I have also found that looking into probabilistic programming is helpful too. These resources are pretty good:

   * The Design and Implementation of Probabilistic Programming Languages (http://dippl.org)

   * Practical Probabilistic Programming (https://smile.amazon.com/Practical-Probabilistic-Programming...)

   The currently most popular ML frameworks are scikit-learn, Tensorflow, Theano and Keras.

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4. abanNov 2016
   Hakaru [0] is another cool probabilistic programming language.

   If you like functional programming, probabilistic functional programming might also be interesting for you. I'd recommend reading Practical Probabilistic Programming with Monads [1], and checking out monad-bayes [2] which builds on the ideas discussed in the paper.

   [0]: https://github.com/hakaru-dev/hakaru

   [1]: http://mlg.eng.cam.ac.uk/pub/pdf/SciGhaGor15.pdf

   [2]: https://github.com/adscib/monad-bayes

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5. eli_gottliebApr 2015
   You should really look into the emerging field of probabilistic programming. Avi Pfeffer has a nice book out on it, Practical Probabilistic Programming (or at least, you can get PDFs by pre-ordering). It basically expands the PGM way of reasoning to Turing-complete domains, and "hides" the problem of coding custom inference algorithms by making them parts of the language runtime.

   My personal prediction is that once we get good at learning whole probabilistic programs from data rather than just inferring free numerical parameters from data, this is going to become the dominant mode of machine reasoning.

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6. probinsoSep 2016
   The PPAML project yielded a lot of cool work, I think that many people are still figuring out the most impact use case. I believe that the most mature new production language is Figaro, out of Charles River. They have some good example projects and a introductory text for their language.

   https://www.cra.com/technical-expertise/probabilistic-modeli...

   https://www.amazon.com/Practical-Probabilistic-Programming-A...

   That being said, I'm very happy that Hakaru is getting attention.

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