WeightWatcher, an open-source diagnostic tool for analyzing Deep Neural Nets - Livestream

WeightWatcher (WW): is an open-source, diagnostic tool for analyzing Deep Neural Networks (DNN), without needing access to training or even test data. It can be used to:
* analyze pre/trained PyTorch, Keras, DNN models (Conv2D and Dense layers)
* monitor models, and the model layers, to see if they are over-trained or over-parameterized
* predict test accuracies across different models, with or without training data
* detect potential problems when compressing or fine-tuning pre-trained models
* layer warning labels: over-trained; under-trained

as well as several new experimental model transformations, including:
* SVDSmoothing: builds a model that can be used to predict test accuracies, but only with the training data.
* SVDSharpness: removes Correlation Traps, which arise from sub-optimal regularization pre-trained models.
https://github.com/CalculatedContent/WeightWatcher

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