Mathematics Natural language processing with deep learning

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Mathematics [From fundamentals to state-of-the-art BERT, Learning natural language processing with deep learning with fun while moving code] / Learn natural language processing with deep learning from the fundamentals to applications while moving code. Through 3 practical tasks of document classification, reputation analysis, and unique expression recognition, you can learn natural language processing with deep learning from both theoretical and implementation aspects. /■ From fundamentals to state-of-the-art BERT / Four deep-learning models, bag-of-embedments, CNN, LSTM, and BERT. / Learn from basic models to state-of-the-art BERT in one book. Model development using /■ Python library "AllenNLP" / Python library for deep-learning natural language processing "AllenNLP" enables you to easily implement natural language processing models without programming from the ground up by writing a JSON-formatted configuration file. From fundamentals to state-of-the-art BERT, "AllenNLP" enables you to easily implement natural language processing models without programming from the ground up. From fundamentals to state-of-the-art BERT, this book provides a thorough explanation of how to implement models using AllenNLP. Using /■ Japanese datasets / Using the Japanese datasets, you can use your models in the field as they are. /■ Google Colab is ready to run / The code used for the commentary is ready to run on a web browser through Google Colab. Google Colab is ready to run on a web browser without building an environment. Google Colab is ready to run on a web browser through Google Colab. Google Colab is ready to run on a web browser through Google Colab.