courseraАнализ данных

Fine Tune BERT for Text Classification with TensorFlow

Курс от Coursera

Средний≈ 2.9 чАнглийский

О курсе

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.

Навыки, которые вы освоите

Tensorflow Fine-tuning Model Evaluation Transfer Learning Deep Learning Natural Language Processing Model Training Keras (Neural Network Library)Data Pipelines Data Preprocessing

Программа курса

1 модулей · 5 учебных материалов

01Fine Tune BERT for Text Classification with TensorFlow5 материалов

Fine Tune BERT for Text Classification with TensorFlow

Project-based Course OverviewЧтение Resources on how BERT worksЧтение Fine Tune BERT for Text Classification with TensorFlowЛабораторная

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Snehan Kekre

Machine Learning Instructor