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This is an implementation of ULMFiT for genomics classification using Pytorch and Fastai. The model architecture used is based on the AWD-LSTM model, consisting of an embedding, three LSTM layers, and a final set of linear layers. The ULMFiT approach uses three training phases to produce a classification model: • Train a language model on a large, unlabeled corpus • Fine tune the language model on the classification corpus • Use the fine tuned language model to initialize a classification model

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Interested in anything related to deep learning, biotech, energy, materials
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