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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
Em um vÃdeo reproduzido em uma tela dividida com a área de trabalho, seu instrutor o orientará sobre esses passos:
Sua área de trabalho é um espaço em nuvem, acessado diretamente do navegador, sem necessidade de nenhum download
Em um vÃdeo de tela dividida, seu instrutor te orientará passo a passo
por JS
14 de dez de 2020Great course. Easy to follow & straightforward explanations.
por VS
12 de abr de 2021The project was well explained and provided good understanding of bert for text classification. Also the quiz were good.
por JH
24 de dez de 2021I have some experience on computer vision and need to take a NLP project, this course give me a heads up on the project.
por YZ
21 de dez de 2021Very detailed explanation on each step, and helped me to get a concrete idea on real world data processing using BERT
