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学生对 Coursera Project Network 提供的 Fine Tune BERT for Text Classification with TensorFlow 的评价和反馈

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166 个评分

课程概述

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....

热门审阅

AA

Dec 12, 2021

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.

SI

Apr 5, 2022

This course can help us to understand BERT for text classification with tensorflow and the material presented is quite easy to follow :)

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1 - Fine Tune BERT for Text Classification with TensorFlow 的 25 个评论(共 33 个)

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