{"action":"created","issue":{"url":"https://api.github.com/repos/JuliaML/Roadmap.jl/issues/9","repository_url":"https://api.github.com/repos/JuliaML/Roadmap.jl","labels_url":"https://api.github.com/repos/JuliaML/Roadmap.jl/issues/9/labels{/name}","comments_url":"https://api.github.com/repos/JuliaML/Roadmap.jl/issues/9/comments","events_url":"https://api.github.com/repos/JuliaML/Roadmap.jl/issues/9/events","html_url":"https://github.com/JuliaML/Roadmap.jl/issues/9","id":163451290,"number":9,"title":"List of existing packages and projects","user":{"login":"ahwillia","id":636625,"avatar_url":"https://avatars.githubusercontent.com/u/636625?v=3","gravatar_id":"","url":"https://api.github.com/users/ahwillia","html_url":"https://github.com/ahwillia","followers_url":"https://api.github.com/users/ahwillia/followers","following_url":"https://api.github.com/users/ahwillia/following{/other_user}","gists_url":"https://api.github.com/users/ahwillia/gists{/gist_id}","starred_url":"https://api.github.com/users/ahwillia/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/ahwillia/subscriptions","organizations_url":"https://api.github.com/users/ahwillia/orgs","repos_url":"https://api.github.com/users/ahwillia/repos","events_url":"https://api.github.com/users/ahwillia/events{/privacy}","received_events_url":"https://api.github.com/users/ahwillia/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":7,"created_at":"2016-07-01T18:33:24Z","updated_at":"2016-07-03T11:47:43Z","closed_at":null,"body":"I want to keep a running list of either machine learning packages in Julia that are being actively developed/maintained. My reasoning is that (a) we have a lot to learn from each others efforts, and (b) it would be great to coordinate efforts going forward.\r\n\r\nPart (b) is of course a bit tricky - having packages that are \"standalone\" is desirable in many ways. But our vision is that it is also desirable to have these packages (or at least a subset of them) play nicely with each other. The hope is that [**Learn.jl**](https://github.com/JuliaML/Learn.jl) will provide a consistent API with backends to different optimization and machine learning packages. We'd like the scope to be as broad as possible, while still being simple enough to be useful and maintainable.\r\n\r\n**Note to collaborators:** *please update and edit this list as you see fit. Also consider cc-ing the authors of those packages so that they are aware.*\r\n\r\n**Note to others:** *we'd love to hear your thoughts and updates on your latest projects. We're happy to add your package to this list, please get in touch with us!* https://gitter.im/JuliaML/chat\r\n\r\n### Neural Nets / Deep Learning\r\n\r\n- [Flimsy.jl](https://github.com/thomlake/Flimsy.jl) (deep learning), cc: @thomlake\r\n- [RecurrentNN.jl](https://github.com/Andy-P/RecurrentNN.jl), cc: @Andy-P\r\n- [DeepQLearning.jl](https://github.com/Andy-P/DeepQLearning.jl)\r\n- [NNGraph.jl](https://github.com/Andy-P/NNGraph.jl)\r\n- [Boltzmann.jl](https://github.com/dfdx/Boltzmann.jl), cc: @dfdx\r\n- [Mocha.jl](https://github.com/pluskid/Mocha.jl), cc: @pluskid \r\n\r\n### Frameworks for representing computations and probability distributions as graphs\r\n\r\nThese should help guide the development of [Transformations.jl](https://github.com/JuliaML/Transformations.jl)\r\n\r\n- [Flow.jl](https://github.com/MikeInnes/Flow.jl), cc: @MikeInnes\r\n- [BayesNets.jl](https://github.com/sisl/BayesNets.jl), cc: @tawheeler\r\n\r\n### Other Stuff\r\n\r\n- [RandomForests.jl](https://github.com/bicycle1885/RandomForests.jl), cc: @bicycle1885 \r\n- [OnlineStats.jl](https://github.com/joshday/OnlineStats.jl), cc: @joshday \r\n- [VinDsl.jl](https://github.com/jmxpearson/VinDsl.jl) (variational inference), cc: @jmxpearson\r\n\r\n**Note:** There are also a lot of excellent statistical modeling packages (e.g. [MultivariateStats.jl](https://github.com/JuliaStats/MultivariateStats.jl), and others). I view these packages as being a slightly different focus from this project -- namely they provide a library of canned methods with a consistent API. I tried to pick packages that aim to create an internal ***framework*** for specifying and fitting a large class of models."},"comment":{"url":"https://api.github.com/repos/JuliaML/Roadmap.jl/issues/comments/230149311","html_url":"https://github.com/JuliaML/Roadmap.jl/issues/9#issuecomment-230149311","issue_url":"https://api.github.com/repos/JuliaML/Roadmap.jl/issues/9","id":230149311,"user":{"login":"Evizero","id":10854026,"avatar_url":"https://avatars.githubusercontent.com/u/10854026?v=3","gravatar_id":"","url":"https://api.github.com/users/Evizero","html_url":"https://github.com/Evizero","followers_url":"https://api.github.com/users/Evizero/followers","following_url":"https://api.github.com/users/Evizero/following{/other_user}","gists_url":"https://api.github.com/users/Evizero/gists{/gist_id}","starred_url":"https://api.github.com/users/Evizero/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/Evizero/subscriptions","organizations_url":"https://api.github.com/users/Evizero/orgs","repos_url":"https://api.github.com/users/Evizero/repos","events_url":"https://api.github.com/users/Evizero/events{/privacy}","received_events_url":"https://api.github.com/users/Evizero/received_events","type":"User","site_admin":false},"created_at":"2016-07-03T11:47:43Z","updated_at":"2016-07-03T11:47:43Z","body":"My core goal is that any researcher / student should be able to switch out any low level part while still being able to use the rest of the highlevel-framework. For example if I were interested in trying out a new Loss, I should be able to write one and still be able to use the rest (such as optimization algorithm and CV) as intended and without performance penalties (so it should be on the same level as \"build-in\" loss functions and not some second class construct). Same goes if I am interested in testing a new way of splitting or resampling a dataset, or a new data source, or writing an optimization algorithm that should work for let's say any margin-based loss function that is convex with a strongly convex and differentiable penalty.\r\n\r\nIn other words, a researcher should be able to focus just on the part that is of his/her research interest, while not having to reimplement all the other stuff to be able to compare it established approaches. I think that is something Julia is uniquely qualified for"}}
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