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Mo Rebaie
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...
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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.
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Provides foundations for intermediate learners
Builds on material in a previous course in a series
Assumes you already know deep learning fundamentals
Teaches essentials of semantic text similarity using Tensorflow
Provides bonus exercises in recurrent neural networks with Tensorflow
Helps you create and evaluate semantic similarity models in texts

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Reviews summary

Tensorflow nlp: semantic similarity

This two-hour long project teaches learners about the fundamentals of semantic similarity in texts. Course-takers get hands-on practice in building a semantic similarity model in texts with Tensorflow, learning to create, visualize, and evaluate text similarity embeddings. Students will also get a bonus exercise about recurrent neural network implemented with Tensorflow.
Includes bonus exercise.
"You will also get a bonus exercise about recurrent neural network implemented with Tensorflow."
Suitable for beginners in NLP.
"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."
Builds skills in TensorFlow.
"By the end of this project, you will have learned how to build a semantic similarity model in texts with Tensorflow."
Develops NLP skills.
"This guided project course is part of the "Tensorflow for Natural Language Processing" series..."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in TensorFlow for NLP: Semantic Similarity in Texts with these activities:
Read 'Deep Learning for Natural Language Processing' by Liu and Jiang
Reading a book like 'Deep Learning for Natural Language Processing' will provide you with a deeper understanding of the field and complement the concepts covered in the course.
Show steps
  • Find a copy of the book.
  • Read the book carefully.
  • Summarize the key points of the book.
Show all one activities

Career center

Learners who complete TensorFlow for NLP: Semantic Similarity in Texts will develop knowledge and skills that may be useful to these careers:
NLP Engineer
NLP Engineers may find this course helpful for learning how to use TensorFlow to evaluate, visualize, and create text similarity embeddings. This course also provides hands-on, practical examples of how to create a semantic similarity model in texts with TensorFlow. It covers the fundamentals of semantic similarity in texts and provides a good foundation for NLP Engineers.
Artificial Intelligence Engineer
Artificial Intelligence Engineers working on NLP-based projects may find this course helpful. This course focuses on how to evaluate, visualize, and create text similarity embeddings with TensorFlow. Learners will also learn how to build a semantic similarity model in texts with TensorFlow. This course focuses on the practical applications of NLP with TensorFlow, which may be helpful for Artificial Intelligence Engineers working on NLP projects.
Machine Learning Engineer
Machine Learning Engineers working on NLP projects may find this course helpful. This course focuses on how to evaluate, visualize, and create text similarity embeddings with TensorFlow. Learners will also learn how to build a semantic similarity model in texts with TensorFlow. This course focuses on the practical applications of NLP with TensorFlow, which may be helpful for Machine Learning Engineers working on NLP projects.
Deep Learning Engineer
Deep Learning Engineers working on NLP-based projects may find this course helpful for building a foundation in using TensorFlow to evaluate, visualize, and create text similarity embeddings. This course also teaches learners how to build a semantic similarity model in texts with TensorFlow. Deep Learning Engineers with an interest in NLP may find this course helpful for furthering their knowledge of NLP with TensorFlow.
Software Engineer
Software Engineers working on NLP-based projects may find this course helpful for building a foundation in using TensorFlow to evaluate, visualize, and create text similarity embeddings. This course also teaches learners how to build a semantic similarity model in texts with TensorFlow. Software Engineers with an interest in NLP may find this course helpful for furthering their knowledge of NLP with TensorFlow.
Data Analyst
Data Analysts working on NLP-based projects may find this course helpful for building a foundation in using TensorFlow to evaluate, visualize, and create text similarity embeddings. This course also teaches learners how to build a semantic similarity model in texts with TensorFlow. Data Analysts with an interest in NLP may find this course helpful for furthering their knowledge of NLP with TensorFlow.
Research Scientist
Research Scientists working in the field of natural language processing may find this course helpful. It provides a very hands-on approach to learning how to use TensorFlow to evaluate and visualize semantic textual similarity. This course also helps build a foundation for creating text similarity embeddings with TensorFlow and creating a semantic similarity model in texts. Researchers interested in NLP may find the Tensorflow exercises in this course helpful.
Data Scientist
Data Scientists seeking to work on natural language processing (NLP) projects may find this course helpful. As NLP is used in a number of fields, the applications of this course may be useful across different industries. In this 2-hour long project-based course, you will learn about fundamentals of semantic similarity in texts and how to use TensorFlow to visualize and evaluate textual similarity. You will also learn how to create and evaluate text similarity embeddings with TensorFlow. By the end of this project, you will have learned how to build a semantic similarity model in texts with TensorFlow.
Technical Writer
Technical Writers working on NLP-based projects may find this course helpful for building a foundation in using TensorFlow to evaluate, visualize, and create text similarity embeddings. This course also teaches learners how to build a semantic similarity model in texts with TensorFlow. Technical Writers with an interest in NLP may find this course helpful for furthering their knowledge of NLP with TensorFlow.
Business Analyst
Business Analysts working on NLP-based projects may find this course helpful for building a foundation in using TensorFlow to evaluate, visualize, and create text similarity embeddings. This course also teaches learners how to build a semantic similarity model in texts with TensorFlow. Business Analysts with an interest in NLP may find this course helpful for furthering their knowledge of NLP with TensorFlow.
Marketing Manager
Marketing Managers working on NLP-based projects may find this course helpful for building a foundation in using TensorFlow to evaluate, visualize, and create text similarity embeddings. This course also teaches learners how to build a semantic similarity model in texts with TensorFlow. Marketing Managers with an interest in NLP may find this course helpful for furthering their knowledge of NLP with TensorFlow.
Copywriter
Copywriters working on NLP-based projects may find this course helpful for building a foundation in using TensorFlow to evaluate, visualize, and create text similarity embeddings. This course also teaches learners how to build a semantic similarity model in texts with TensorFlow. Copywriters with an interest in NLP may find this course helpful for furthering their knowledge of NLP with TensorFlow.
Content Writer
Content Writers working on NLP-based projects may find this course helpful for building a foundation in using TensorFlow to evaluate, visualize, and create text similarity embeddings. This course also teaches learners how to build a semantic similarity model in texts with TensorFlow. Content Writers with an interest in NLP may find this course helpful for furthering their knowledge of NLP with TensorFlow.
Project Manager
Project Managers working on NLP-based projects may find this course somewhat helpful. This course focuses on how to evaluate, visualize, and create text similarity embeddings with TensorFlow. Learners will also learn how to build a semantic similarity model in texts with TensorFlow. This course focuses on the practical applications of NLP with TensorFlow, which may be helpful for Project Managers working on NLP projects.
Product Manager
Product Managers working on NLP-based projects may find this course somewhat helpful. This course focuses on how to evaluate, visualize, and create text similarity embeddings with TensorFlow. Learners will also learn how to build a semantic similarity model in texts with TensorFlow. This course focuses on the practical applications of NLP with TensorFlow, which may be helpful for Product Managers working on NLP projects.

Reading list

We've selected seven books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in TensorFlow for NLP: Semantic Similarity in Texts.
Provides a comprehensive overview of statistical natural language processing. It covers a wide range of topics, from basic concepts to advanced techniques.
Provides a comprehensive overview of deep learning techniques for natural language processing. It covers a wide range of topics, from word embeddings to transformer models.
Provides a comprehensive overview of speech and language processing. It covers a wide range of topics, from basic concepts to advanced techniques.
Provides a comprehensive introduction to natural language processing, covering topics such as text classification, information retrieval, and machine translation. It includes a number of hands-on exercises that will help you to develop your own NLP models.
Provides a comprehensive overview of deep learning techniques for natural language processing. It covers a wide range of topics, from basic concepts to advanced techniques.
Provides a comprehensive overview of natural language understanding. It covers a wide range of topics, from basic concepts to advanced techniques.
Provides a comprehensive overview of machine learning techniques for natural language processing. It covers a wide range of topics, from basic concepts to advanced techniques.

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