{"id":668421,"date":"2020-06-19T00:00:51","date_gmt":"2020-06-19T07:00:51","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=668421"},"modified":"2022-10-14T08:29:19","modified_gmt":"2022-10-14T15:29:19","slug":"neuronblocks-building-your-nlp-dnn-models-like-playing-lego","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/neuronblocks-building-your-nlp-dnn-models-like-playing-lego\/","title":{"rendered":"NeuronBlocks: Building Your NLP DNN Models Like Playing Lego"},"content":{"rendered":"
Deep Neural Networks (DNN) have been widely employed in industry to address various Natural Language Processing (NLP) tasks. However, many engineers find it a big overhead when they have to choose from multiple frameworks, compare different types of models, and understand various optimization mechanisms. 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