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Adversarial Learning - Presentation.pdf
Adversarial Workshop - Adversarial Examples for MNIST.ipynb
Adversarial Workshop - Adversarial Examples for Sentiment.ipynb
Adversarial Workshop - Dropout Classification.ipynb
Adversarial Workshop - Dropout Credit.ipynb
Adversarial Workshop - Dropout Intuition.ipynb
README.md

README.md

Adversarial Workshop

TensorFlow has taken the deep learning world by storm. This workshop will be led by one of TensorFlow’s main contributors, Illia Polosukhin. Illia’s hands-on workshop will cover:

  • Dropout - both for preventing overfitting and as mechanics to get "what model doesn't know" (confidence of prediction).

  • Augmenting data with adversarial examples - to prevent overfitting and speed up training

  • How to limit technical exploits of your models - e.g. how to use different methods to prevent your model going haywire, using different methods (confidence, adversarial examples, discriminator, separate classifiers or just simple whitelists).

References

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