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anomalyDetectionImages
anomalyDetectionImages
 
 
concepts
concepts
 
 
contentRetrievalImages
contentRetrievalImages
 
 
notebook_images
notebook_images
 
 
util
util
 
 
01-MNIST_Fully_Connected_tutorial.ipynb
01-MNIST_Fully_Connected_tutorial.ipynb
 
 
02-MNIST_testing_FC_architectures.ipynb
02-MNIST_testing_FC_architectures.ipynb
 
 
03-MNIST_FC_vs_Conv.ipynb
03-MNIST_FC_vs_Conv.ipynb
 
 
04-layers.ipynb
04-layers.ipynb
 
 
05-validation_set.ipynb
05-validation_set.ipynb
 
 
06.0_balance.ipynb
06.0_balance.ipynb
 
 
06.1_balanced evaluation.ipynb
06.1_balanced evaluation.ipynb
 
 
07.0-image_processing_for_data_augmentation.ipynb
07.0-image_processing_for_data_augmentation.ipynb
 
 
07.1-data_augmentation.ipynb
07.1-data_augmentation.ipynb
 
 
08-ensembles.ipynb
08-ensembles.ipynb
 
 
09-transferLearning.ipynb
09-transferLearning.ipynb
 
 
10-ROC.ipynb
10-ROC.ipynb
 
 
11.0-autoencoders.ipynb
11.0-autoencoders.ipynb
 
 
11.1-colorization.ipynb
11.1-colorization.ipynb
 
 
12-VAE.ipynb
12-VAE.ipynb
 
 
13-CVAE.ipynb
13-CVAE.ipynb
 
 
14-UNet-segmentation.ipynb
14-UNet-segmentation.ipynb
 
 
15 - GAN.ipynb
15 - GAN.ipynb
 
 
16 - WGAN-GP.ipynb
16 - WGAN-GP.ipynb
 
 
17 - Adversarial attacks.ipynb
17 - Adversarial attacks.ipynb
 
 
README.md
README.md
 
 

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Deep Learning Course

This repo contains a set of notebooks used in a Deep Learning Course at University of Minho. The course is focused on image classification and generative models.

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Course on Deep Learning mostly focused in computer vision with convolutional neural networks

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computer-vision deep-learning pytorch convolutional-neural-networks

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