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A PyTorch wrapper to allow for rapid experimentation and development of deep learning models. In addition to accepted techniques like cyclical learning rate scheduling and Bloom embeddings, this will also be a place for some of my more experimental features like spatial attention layers or weighted tabular residual blocks.
A PyTorch wrapper to allow for rapid experimentation and development of deep learning models. In addition to accepted techniques like cyclical learning rate scheduling and Bloom embeddings, this will also be a place for some of my more experimental features like spatial attention layers or weighted tabular residual blocks.