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This is a guided project on fine-tuning a Bidirectional Transformers for Language Understanding (BERT) model for text classification with TensorFlow. In this 2.5 hour long project, you will learn to preprocess and tokenize data for BERT classification, build TensorFlow input pipelines for text data with the tf.data API, and train and evaluate a fine-tuned BERT model for text classification with TensorFlow 2 and TensorFlow Hub. Prerequisites: In order to successfully complete this project, you should be competent in the Python programming language, be familiar with deep learning for Natural Language Processing (NLP), and have trained models with TensorFlow or and its Keras API. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
natural-language-processing
Tensorflow
machine-learning
deep-learning
BERT
На видео, которое откроется рядом с рабочей областью, преподаватель объяснит эти шаги:
Ваше рабочее пространство — это облачный рабочий стол в браузере. Ничего не нужно загружать.
На разделенном экране видео преподаватель предоставляет пошаговые
от партнера VS
12 апр. 2021 г.The project was well explained and provided good understanding of bert for text classification. Also the quiz were good.
от партнера DA
15 апр. 2021 г.really nice glue to connect all the dots. Thanks so much
от партнера AA
12 дек. 2021 г.Excellent and very helpful course, the instructor language is very clear and concise and to the point, I would love to learn more from the same instructor.
от партнера YZ
21 дек. 2021 г.Very detailed explanation on each step, and helped me to get a concrete idea on real world data processing using BERT
