This page is to make available Deep Learning models, codes and other resources 

The Main ingredients for running the examples are:

Tensorflow
Keras
anaconda python 3
matplotlib
numpy

Deep Learning models used in the paper “Deep Learning Assessment of galaxy morphology in S-PLUS DataRelease 1 “

Models:  EfficientNetB2 (3 Bands – pretrained), EfficientNetB2 (5 Bands), EfficientNetB2 (8 Bands), EfficientNetB2 (12 Bands) 

Catalogs:  https://github.com/cdebom/splus_morph

Regular Deep Learning Graduate classes @ CBPF in 2020.b

Slides:
Lecture01 Lecture02 Lecture03 Lecture04 Lecture05 Lecture06 Lecture07
Lecture08 Lecture09 Lecture10 Lecture11 Lecture12 Lecture13 Lecture14

Student’s projects:

Star/Galaxy separation (Igor Reis) 
Semantic Segmentation of  CT scans ( Sheila Monteiro) 
Indoor Localization (Victor Fonseca)
Credit Card Fraud Detection (Filipe Melo)
Few-Shot Learning Lensing Finder (Kayque Teles)
X- Ray Classification (Vitor Machado) 
Wine quality (Alan Almeida) White, Red
Credit Card Fraud Detection (João Valeriano)

Examples and tasks in the Deep Learning classes @CBPF in 2020.b

MNIST Classification 
Mini Classification Data Challenge
Diabetes prediction
Deep Learning from the scratch (Not using any DL lib)
House pricing prediction