Deep Learning Minicourse

This is a 8 hours lectures + 8 hours hands-on with tutors.
The Main idea is to give a brief, but useful, introduction to Deep Learning.  The particular focus/motivation is Astronomy. If you are not an astronomer do not worry you should be safe. The only thing you might miss is the scientific motivation behind the examples and a deeper understanding on the dataset.


The Main ingredients for running the examples are:

anaconda python 3

Presentations (English only):

Lecture 01
Lecture 02
Lecture 03
Lecture 04

Presentations at XII CBPF School version:

From July 29 to Aug 2 I presented a different  version of the minicourse at XII CBPF School. The first talk was given by Dr. Márcio Albuquerque


Video (youtube – portuguese):

Lecture 01
Lecture 02
Lecture 03
Lecture 04

Shareable Examples (Portuguese: Exemplos Compartilháveis)

0. The Simplest example I know

  1. Strong Lensing Finder (Image Classification)
  2.  Resnet50 Strong Lensing Finder (Image Classification)
  3. Unsupervised Galaxy Classification from spectra (Auto Encoders and PCA)
  4. Error with Concrete Dropout
  5. PSF removal (GAN)

Credit: The examples 1-5 were written by Luciana Olívia Dias and Patrick Schubert under my supervision. There is a similar version of example “0”  from here.

Datasets for the Examples (Portuguese: Dados necessários para os exemplos)

lensdataset for colabs 1 and 2. (Sorry for the zip, wordpress do not allow tar.gz)
spec4000.npz.tar.gz for colab 3
PSF removal GAN for colab 5
For fits image visualization you may use: DS9
To produce beautiful pics from fits you may use: Trilogy

The Lens dataset is a preprocessed small portion of the one produced by Bologna Lens Factory Team for the Strong Lensing Challenge.

The galaxy spectra is from sloan, from Astro-ML examples.

The dataset for colab 5 is from arXiv:1702.00403.