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Comentários e feedback de alunos de Convolutions for Text Classification with Keras da instituição Coursera Project Network

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Sobre o curso

Welcome to this hands-on, guided introduction to Text Classification using 1D Convolutions with Keras. By the end of this project, you will be able to apply word embeddings for text classification, use 1D convolutions as feature extractors in natural language processing (NLP), and perform binary text classification using deep learning. As a case study, we will work on classifying a large number of Wikipedia comments as being either toxic or not (i.e. comments that are rude, disrespectful, or otherwise likely to make someone leave a discussion). This issue is especially important, given the conversations the global community and tech companies are having on content moderation, online harassment, and inclusivity. The data set we will use comes from the Toxic Comment Classification Challenge on Kaggle. To complete this guided project, we recommend that you have prior experience in Python programming, deep learning theory, and have used either Tensorflow or Keras to build deep learning models. We assume you have this foundational knowledge and want to learn how to use convolutions in NLP tasks such as classification. Note: This course works best for learners based in the North America region. We’re currently working on providing the same experience in other regions....

Melhores avaliações

RT

25 de jul de 2020

Good explanation about how to work with pretrained embeddings, but works too slow

RD

14 de jul de 2020

Very good project to understand the use of convolution in text data.

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1 — 14 de 14 Avaliações para o Convolutions for Text Classification with Keras

por Kinjal S P

30 de set de 2020

por RUDRA P D

15 de jul de 2020

por Mesut Y

23 de nov de 2020

por Yaron K

18 de jun de 2021

por Ruslan T

26 de jul de 2020

por Gangone R

3 de jul de 2020

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8 de jun de 2022

por Md. R Q S

17 de set de 2020

por tale p

28 de jun de 2020

por Md. R A

27 de jun de 2020

por Ashwin P

26 de jun de 2020

por p s

26 de jun de 2020

por Simon S R

4 de set de 2020

por Liao H

12 de jul de 2020