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Отзывы учащихся о курсе Basic Sentiment Analysis with TensorFlow от партнера Coursera Project Network

Оценки: 197
Рецензии: 32

О курсе

Welcome to this project-based course on Basic Sentiment Analysis with TensorFlow. In this project, you will learn the basics of using Keras with TensorFlow as its backend and you will learn to use it to solve a basic sentiment analysis problem. By the end of this 2-hour long project, you will have created, trained, and evaluated a Neural Network model that, after the training, will be able to predict movie reviews as either positive or negative reviews - classifying the sentiment of the review text. Notes: - 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....

Лучшие рецензии


6 авг. 2020 г.

A very good explanation for basic sentiment analysis using TensorFlow and Keras. One suggestion, the explanation video on a guided project would be great if there is a subtitle


1 июня 2020 г.

Fantastic! This got me really excited to get into a deeper understanding of TensorFlow and neural networks and overall ML

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26–32 из 32 отзывов о курсе Basic Sentiment Analysis with TensorFlow

автор: Gurpreet S C

20 апр. 2020 г.


автор: Debolina

10 авг. 2020 г.

The explanation could have been better for the parts involving Deep Learning. Nevermind, it was a good course. I enjoyed implementing this project. Thank you!

автор: Taher K

8 июля 2020 г.

Overall it was useful. I learned Embedding coding. The last parts (6, 7) were a little bit confusing and need more explanation.

автор: Paradorn B

3 июня 2020 г.

Would like to explain the theory And additional applications.

автор: Priyansh K

13 мая 2020 г.

Very slow interface

автор: Mohammad H

9 апр. 2020 г.

As instruction or organization you have to support the course and project with more explanation about the functions/classes... and what is the meaning of each function input and what is the output meaning.

автор: Ransaka R

6 июня 2020 г.

Not performed well