Chevron Left
Sentiment Analysis with Deep Learning using BERT に戻る

Coursera Project Network による Sentiment Analysis with Deep Learning using BERT の受講者のレビューおよびフィードバック

4.4
52件の評価
11件のレビュー

コースについて

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....

人気のレビュー

フィルター:

Sentiment Analysis with Deep Learning using BERT: 1 - 11 / 11 レビュー

by Shanshan W

May 18, 2020

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

by Dr. P W

May 31, 2020

Good course using BERT technique

by AMIT K S

May 31, 2020

Fun and knowledgeable Course

by Rishabh R

May 06, 2020

Excellent

by 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!

by Syed A G S

May 16, 2020

its very helpful and very good

by KRUSHNA P S

May 30, 2020

Awesome Project

by Ovi S

Jun 02, 2020

Good

by VENKATA N C K

Jun 01, 2020

.

by Mogan P K

May 25, 2020

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

by Vaibhav J

May 30, 2020

Could Have been better