### Cosmology Lectures at XIII CBPF School (2021):

From Aug 9 to Aug 13 I presented a different “Cosmology with python” minicourse at XIII CBPF School

XIII-School-CBPF-01

XIII-School-CBPF-02

XIII-School-CBPF-03

XIII-School-CBPF-04

XIII-School-CBPF-05

Notebooks and scripts:

1. Hubble Constant fit Dataset for task 01 : hubble

2. Friedmann Eq

3. Cosmological parameters with Supernova

4. Strong Lensing Constraints

5. Photometric redshifts Dataset

7. Combining Observational probes

Example 1 adapted from:here

### Deep Learning Lectures at XIII CBPF School version (2021):

From Aug 9 to Aug 13 I presented a different version of the minicourse at XIII CBPF School with prof. Elisangela L. Faria.

XIII-School-CBPF-01

XIII-School-CBPF-02

XIII-School-CBPF-03

XIII-School-CBPF-04

XIII-School-CBPF-05

Notebooks and scripts:

1. Lecture 01 slides

2. Lecture 02 the slides

5. Autoencoder

### Deep Learning Class at Data Science course (IAG – 2021 -English):

### Previous version of 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:

Tensorflow

Keras

anaconda python 3

matplotlib

numpy

### Presentations (English only):

Lecture 01

Lecture 02

Lecture 03

Lecture 04

### Presentations at XII CBPF School version (2019):

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

XII-School-CBPF-01

XII-School-CBPF-02

XII-School-CBPF-03

XII-School-CBPF-04

Video (youtube – portuguese):

Lecture 01

Lecture 02

Lecture 03

Lecture 04

### Shareable Examples (Portuguese: Exemplos Compartilháveis)

0. The Simplest example I know

- Strong Lensing Finder (Image Classification)
- Resnet50 Strong Lensing Finder (Image Classification)
- Unsupervised Galaxy Classification from spectra (Auto Encoders and PCA)
- Error with Concrete Dropout
- 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 adventuresinmachinelearning.com 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

*Credit:*

*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.

* *