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Fine Tune BERT for Text Classification with TensorFlow

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.
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Length 2 weeks
Effort 2.5 hours
Starts Dec 14 (6 weeks ago)
Cost $9
From Coursera Project Network via Coursera
Instructor Snehan Kekre
Download Videos On all desktop and mobile devices
Language English
Subjects Data Science Programming
Tags Data Science Data Analysis Machine Learning

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Rating Not enough ratings
Length 2 weeks
Effort 2.5 hours
Starts Dec 14 (6 weeks ago)
Cost $9
From Coursera Project Network via Coursera
Instructor Snehan Kekre
Download Videos On all desktop and mobile devices
Language English
Subjects Data Science Programming
Tags Data Science Data Analysis Machine Learning

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