{"id":20407616,"url":"https://github.com/phuijse/tutorial_pyro_astronomy","last_synced_at":"2025-07-05T04:35:31.728Z","repository":{"id":117592578,"uuid":"319755882","full_name":"phuijse/tutorial_pyro_astronomy","owner":"phuijse","description":"A tutorial on probabilistic models based on deep neural networks using Pytorch and Pyro for astronomical time series data","archived":false,"fork":false,"pushed_at":"2022-12-01T13:23:29.000Z","size":12383,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-05T02:31:55.386Z","etag":null,"topics":["astronomy","pyro","pytorch","time-series","tutorial"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/phuijse.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2020-12-08T20:41:49.000Z","updated_at":"2022-12-01T13:26:50.000Z","dependencies_parsed_at":null,"dependency_job_id":"099e028c-ff26-45e6-b17c-6753266252a8","html_url":"https://github.com/phuijse/tutorial_pyro_astronomy","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/phuijse/tutorial_pyro_astronomy","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/phuijse%2Ftutorial_pyro_astronomy","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/phuijse%2Ftutorial_pyro_astronomy/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/phuijse%2Ftutorial_pyro_astronomy/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/phuijse%2Ftutorial_pyro_astronomy/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/phuijse","download_url":"https://codeload.github.com/phuijse/tutorial_pyro_astronomy/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/phuijse%2Ftutorial_pyro_astronomy/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":261652887,"owners_count":23190360,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["astronomy","pyro","pytorch","time-series","tutorial"],"created_at":"2024-11-15T05:25:20.874Z","updated_at":"2025-06-24T10:36:53.754Z","avatar_url":"https://github.com/phuijse.png","language":"Jupyter Notebook","readme":"# Deep Probabilistic Models with applications in astronomy\n\nIn this tutorial we will review the basics of inference with probabilistic models, the more recent \"deep\" probabilistic models and how to implement them using the [Pyro probabilistic programming library](http://docs.pyro.ai/en/stable/getting_started.html). \n\nAfter that we will have a hands-on experience training probabilistic models to analyze time series from astronomical survey projects.\n\nTo install the dependencies I suggest to use conda:\n\n    conda env create -f environment.yml\n    \nAnd then launch \n\n    jupyter notebook\n    \nAnd navigate to `tutorial.ipynb`\n\n\nYou can also open this tutorial [in google colab](https://colab.research.google.com/drive/1-RfsaAUnQ6foX6yGHGbbp_e_P5zR1dVv?usp=sharing)\n\nFor more on these topics see:\n\n- [More material and code examples on this topic](https://github.com/phuijse/BLNNbook)\n- [Lecture slides on neural networks (in spanish)](https://docs.google.com/presentation/d/1IJ2n8X4w8pvzNLmpJB-ms6-GDHWthfsJTFuyUqHfXg8/edit?usp=sharing)\n\nAuthor: Pablo Huijse, phuijse at inf dot uach dot cl\n\nThis tutorial was presented online at the IEEE Summer School on Computational Intelligence 2020, hosted by UFRO. For more activities organized by the IEEE Chile CIS chapter see: https://cis.ieeechile.cl/\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fphuijse%2Ftutorial_pyro_astronomy","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fphuijse%2Ftutorial_pyro_astronomy","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fphuijse%2Ftutorial_pyro_astronomy/lists"}