https://github.com/phuijse/tutorial_pyro_astronomy
A tutorial on probabilistic models based on deep neural networks using Pytorch and Pyro for astronomical time series data
https://github.com/phuijse/tutorial_pyro_astronomy
astronomy pyro pytorch time-series tutorial
Last synced: 3 months ago
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A tutorial on probabilistic models based on deep neural networks using Pytorch and Pyro for astronomical time series data
- Host: GitHub
- URL: https://github.com/phuijse/tutorial_pyro_astronomy
- Owner: phuijse
- License: mit
- Created: 2020-12-08T20:41:49.000Z (almost 5 years ago)
- Default Branch: main
- Last Pushed: 2022-12-01T13:23:29.000Z (almost 3 years ago)
- Last Synced: 2025-03-05T02:31:55.386Z (7 months ago)
- Topics: astronomy, pyro, pytorch, time-series, tutorial
- Language: Jupyter Notebook
- Homepage:
- Size: 11.8 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Deep Probabilistic Models with applications in astronomy
In 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).
After that we will have a hands-on experience training probabilistic models to analyze time series from astronomical survey projects.
To install the dependencies I suggest to use conda:
conda env create -f environment.yml
And then launch
jupyter notebook
And navigate to `tutorial.ipynb`
You can also open this tutorial [in google colab](https://colab.research.google.com/drive/1-RfsaAUnQ6foX6yGHGbbp_e_P5zR1dVv?usp=sharing)
For more on these topics see:
- [More material and code examples on this topic](https://github.com/phuijse/BLNNbook)
- [Lecture slides on neural networks (in spanish)](https://docs.google.com/presentation/d/1IJ2n8X4w8pvzNLmpJB-ms6-GDHWthfsJTFuyUqHfXg8/edit?usp=sharing)
Author: Pablo Huijse, phuijse at inf dot uach dot cl
This 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/