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Opiniones y comentarios de aprendices correspondientes a Basic Sentiment Analysis with TensorFlow por parte de Coursera Project Network

4.5
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187 calificaciones
32 reseña

Acerca del Curso

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....

Principales reseñas

IS

Aug 07, 2020

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

AT

Jun 02, 2020

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 de 32 revisiones para Basic Sentiment Analysis with TensorFlow

por Gurpreet S C

Apr 20, 2020

good

por Debolina M

Aug 10, 2020

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!

por Seyed T

Jul 08, 2020

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

por Paradorn B

Jun 03, 2020

Would like to explain the theory And additional applications.

por Priyansh K

May 13, 2020

Very slow interface

por Mohammad S H

Apr 09, 2020

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.

por Ransaka R

Jun 06, 2020

Not performed well