Linking pages
- What Explainable AI fails to explain (and how we fix that) | by Alvin Wan | Towards Data Science https://towardsdatascience.com/what-explainable-ai-fails-to-explain-and-how-we-fix-that-1e35e37bee07 1 comment
- GitHub - jphall663/awesome-machine-learning-interpretability: A curated list of awesome machine learning interpretability resources. https://github.com/jphall663/awesome-machine-learning-interpretability 0 comments
- Making Decision Trees Accurate Again: Explaining What Explainable AI Did Not – The Berkeley Artificial Intelligence Research Blog https://bair.berkeley.edu/blog/2020/04/23/decisions/ 0 comments
Linked pages
- What is backpropagation really doing? | Chapter 3, Deep learning - YouTube https://www.youtube.com/watch?v=Ilg3gGewQ5U 203 comments
- Yes you should understand backprop | by Andrej Karpathy | Medium https://medium.com/@karpathy/yes-you-should-understand-backprop-e2f06eab496b 108 comments
- http://brohrer.github.io/how_convolutional_neural_networks_work.html 52 comments
- MNIST Demos on Yann LeCun's website http://yann.lecun.com/exdb/lenet/ 2 comments
- [1511.06581] Dueling Network Architectures for Deep Reinforcement Learning http://arxiv.org/abs/1511.06581 0 comments
- [1312.6034] Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps https://arxiv.org/abs/1312.6034 0 comments
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Search title: Saliency Maps for Deep Learning: Vanilla Gradient | by Andrew Schreiber | Medium
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