Monday, June 24, 2019

Data science: resources

Resources:


Other resources:

Original gan paper 


some news & applications
Generative Adversarial Networks recover features in astrophysical images of galaxies beyond the 
deconvolution limit
https://machinelearningmastery.com/impressive-applications-of-generative-adversarial-networks/
http://karpathy.github.io/neuralnets/

Other Links:


Articles:
JP's work:

Books (math):

In physics and related fields:


  • PyTransport: A Python package for the calculation of inflationary correlation functions,
  • http://roban.github.io/CosmoloPy/
  • Python CAMB
  • https://ascl.net/;
  • http://www.usm.uni-muenchen.de/people/paech/Astro_Num_Lab/
  • http://python4astronomers.github.io/
  • https://www.coursera.org/learn/data-driven-astronomy
  • https://sydney.edu.au/news-opinion/news/2017/02/22/data-driven-astronomy.html
  • https://phys.org/news/2018-09-machine-astronomy.html
  • https://physicstoday.scitation.org/doi/pdf/10.1063/PT.3.3261
  • https://www.analyticsindiamag.com/why-machine-learning-is-a-great-career-jump-for-physicists/
  • https://physicsworld.com/a/a-machine-learning-revolution/
  • https://machine-learning-for-physicists.org/
  • https://physics.bu.edu/~pankajm/MLnotebooks.html
  • https://www.uwa.edu.au/research/machine-learning-applications-for-physical-sciences
  • http://www.ipam.ucla.edu/programs/workshops/machine-learning-for-physics-and-the-physics-of-learning-tutorials/
  • http://physics.bu.edu/~pankajm/PY895-ML.html
  • https://towardsdatascience.com/my-journey-from-physics-into-data-science-5d578d0f9aa6
  • http://darkmachines.org/
  • https://www.symmetrymagazine.org/article/studying-the-stars-with-machine-learning
  • https://arxiv.org/abs/1803.08823
  • https://arxiv.org/abs/1808.04728
  • https://arxiv.org/abs/1902.00522
  • https://arxiv.org/abs/1903.10563
  • https://arxiv.org/abs/1904.07248
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