Convolutions for Text Classification with Keras

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En este proyecto guiado, tú:
1.5 hours
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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.

Habilidades que desarrollarás

  • natural-language-processing

  • embeddings

  • deep-learning

  • text-classification

  • keras

Aprende paso a paso

En un video que se reproduce en una pantalla dividida con tu área de trabajo, tu instructor te guiará en cada paso:

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En un video de pantalla dividida, tu instructor te guía paso a paso

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