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