Applied Deep Learning 2024 - Lecture 9 - Preprocessing, Augmentation, Regularization, Visualization

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In this lecture, we're discussing how preprocessing can help our networks to learn better or even enable efficient processing in the first place. Data augmentation is another very important technique that helps us to deal with situations where we don't have a lot of data, or want our models to become more robust to small variations. Regularization is another important trick in our toolbox to prevent overfitting and finally, we have a look at how we can visualize what the models are actually learning. Complete Playlist: https://www.youtube.com/playlist?list=PLNsFwZQ_pkE8tSQuU3jN71fmmGFFCi7Dc == Literature == 1. Chatzimichailidis et al. GradVis: Visualization and Second Order Analysis of Optimization Surfaces during the Training of Deep Neural Networks. 2019 2. Nikolenko. Synthetic Data for Deep Learning. 2019 3. Pramerdorfer. Deep Learning for Visual Computing. 2016 4. Zoph et al. Learning Data Augmentation Strategies for Object Detection. 2019 5. Wu et al. Making and Invisibility Cloak: Real World Adversarial Attacks on Object Detectors. 2019 6. Goodfellow, et al. Explaining and Harnessing Adversarial Examples. 2015. 7. Nicholas et al. DocCreator: A New Software for Creating Synthetic Ground-Truthed Document Images. 2017 8. Ma. Data Augmentation for Audio. 2019. 9. Park et al. SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition. 2019. 10. Zach C. State of the Art Audio Data Augmentation with SpecAugment and PyTorch. 2019. 11. Kanburoğlu. Audio Data Augmentation. 2018 12. Srivastava, et al. Dropout: A Simple Way to Prevent Neural Networks from Overfitting. 2014 13. Wei, et al. EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks. 2019. 14. George Washington Papers. https://www.loc.gov/resource/mgw1b.481/?sp=68 15. Prince. Computer Vision Models. 2012 16. Xie, et al. Unsupervised Data Augmentation for Consistency Training. 2019 17. DeVries, etal. Improved Regularization of Convolutional Neural Netwo

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