TensorFlow for NLP: Semantic Similarity in Texts

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Coursera Project Network
在此指导 项目中,您将:

Learn the fundamentals of Semantic Similarity

Learn how to build a Semantic Textual Similarity Model

Learn how to build NLP models with Tensorflow

Clock2 hours
Comment Dots英语(English)

This guided project course is part of the "Tensorflow for Natural Language Processing" series, and this series presents material that builds on the third course of DeepLearning.AI TensorFlow Developer Professional Certificate, which will help learners reinforce their skills and build more projects with Tensorflow. In this 2-hour long project-based course, you will learn the fundamentals of semantic similarity in texts, and you will learn practically how to use visualize and evaluate semantic textual similarity in the real world and create, visualize, and evaluate text similarity embeddings with Tensorflow in texts, and you will get a bonus exercise about recurrent neural network implemented with Tensorflow. By the end of this project, you will have learned how to build a semantic similarity model in texts with Tensorflow. This class is for learners who want to learn how to work with natural language processing and use Python for building textual models with TensorFlow, and for learners who are currently taking a basic deep learning course or have already finished a deep learning course and are searching for a practical deep learning project with TensorFlow. Also, this project provides learners with further knowledge about creating and evaluating semantic similarity models and improves their skills in Tensorflow which helps them in fulfilling their career goals by adding this project to their portfolios.


Natural Language ProcessingDeep LearningSemantic SimilarityDocument ClassificationTensorflow



  1. Introduction and Overview of the Project

  2. Import Libraries and Create Text Representations

  3. Create and Visualize Semantic Similarity

  4. Download the Data for Semantic Similarity

  5. Evaluate the Semantic Textual Similarity






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