Optimizing Deep Neural Networking with Delve

Optimizing Deep Neural Networking with Delve

Identifying the number of hidden units in a fully connected layer is considered a heuristically-guided craft. A PyTorch library, Delve, was developed that allows identifying the degree of over-parameterization of a layer, thus guiding architecture selection. The library compares the intrinsic dimensionality of the layer over training, providing the user with live feedback during training.

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