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学生对 Coursera Project Network 提供的 Sentiment Analysis with Deep Learning using BERT 的评价和反馈

60 个评分
12 条评论


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....



1 - Sentiment Analysis with Deep Learning using BERT 的 14 个评论(共 14 个)

创建者 Shanshan W

May 18, 2020

The instructor explains very well on how to using bert to train a sentiment classifier. Very cool project.

创建者 Dr. P W

May 31, 2020

Good course using BERT technique

创建者 AMIT K S

May 31, 2020

Fun and knowledgeable Course

创建者 Rishabh R

May 06, 2020


创建者 Ravinder S

May 30, 2020

Ari Anastassiou has done very well to keep is crisp and has taken great care in explaining the implementation. His style is lucid and sincere. I would recommend this short course to anyone who needs an introduction to this heavy concept in a simple and less intimidating manner. Nice work by Ari ! I would love to see a pithy tutorial from him( may be 30 mins) to explain the concepts of BERT as well. This could make it a PERFECT 10 for me. Thank you!

创建者 Shantanu B

Jun 05, 2020

Need major improvement with the interactive notebook along with its response time and UI

创建者 Ramkumar R

Jun 05, 2020

Very useful to learn more with deep learning using Bert

创建者 Syed A G S

May 16, 2020

its very helpful and very good


May 30, 2020

Awesome Project

创建者 Ovi S

Jun 02, 2020



Jun 01, 2020


创建者 Ali A

Jun 04, 2020

more theory needed. Also some benchmarks can be added to show in which ways Bert outperforms others.

创建者 Mogan P K

May 25, 2020

More explanations on the functions and libraries used will make this project better

创建者 Vaibhav J

May 30, 2020

Could Have been better