Sentiment Analysis with Deep Learning using BERT
In this 2-hour long project, you will learn how to analyze a dataset for sentiment analysis. You will learn how to read in a PyTorch BERT model, and adjust the architecture for multi-class classification. You will learn how to adjust an optimizer and scheduler for ideal training and performance. In fine-tuning this model, you will learn how to design a train and evaluate loop to monitor model performance as it trains, including saving and loading models. Finally, you will build a Sentiment Analysis model that leverages BERT's large-scale language knowledge. 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
由 BP 提供2020年9月13日
Very effective course to understand the concept of sentiment analysis using Deep Learning.. Thank you team
由 WM 提供2021年2月12日
Good instructor, however anyone join this must have at least a knowledge in basic Python Programming and have learned about BERT and fundamental of Natural Language Processing
由 NC 提供2020年7月18日
I didn't like the platform you use. Rhyme, it's not a good tool.
由 RT 提供2020年7月18日
Cool tutorial. It's more clear now how to work with BERT