AI Practitioner: Practical BERT Examples

Artificial Intelligence
  • 16 Videos | 50m 7s
  • Includes Assessment
  • Earns a Badge
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Bidirectional Encoder Representations from Transformers (BERT) can be implemented in various ways, and it is up to AI practitioners to decide which one is the best for a particular product. It is also essential to recognize all of BERT's capabilities and its full potential in NLP. In this course, you'll outline the theoretical approaches to several BERT use cases before illustrating how to implement each of them. In full, you'll learn how to use BERT for search engine optimization, sentence prediction, sentence classification, token classification, and question answering, implementing a simple example for each use case discussed. Lastly, you'll examine some fundamental guidelines for using BERT for content optimization.

WHAT YOU WILL LEARN

  • discover the key concepts covered in this course
    name practical approaches to improving search using BERT
    describe how BERT functions inside a search engine
    demonstrate how BERT can be used to search the text of a given document
    describe how we can use BERT for next sentence prediction
    use BERT and Python for next sentence prediction via a PyTorch implementation of BERT
    outline how BERT can be used for sequence classification
    work with BERT to implement a sequence classifier
  • describe how multiple-choice reading comprehension can be done using BERT
    use BERT and Python to implement multiple choice examples via a PyTorch implementation of BERT
    outline how to utilize BERT for token classification
    work with BERT to implement a token classifier
    describe how to develop a question-answering machine using BERT
    work with BERT to implement a question-answering machine
    outline some fundamental guidelines for content optimization using BERT
    summarize the key concepts covered in this course

IN THIS COURSE

  • Playable
    1. 
    Course Overview
    2m 36s
    UP NEXT
  • Playable
    2. 
    Search Improvement Using BERT
    3m 40s
  • Locked
    3. 
    How BERT is Used in Searches
    2m 34s
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    4. 
    Implementing a Mini Search Engine Using BERT
    3m 28s
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    5. 
    BERT for Next Sentence Prediction
    2m 51s
  • Locked
    6. 
    Using BERT and Python for Next Sentence Prediction
    3m 52s
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    7. 
    BERT for Sequence Classification
    3m 35s
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    8. 
    Implementing a Sequence Classifier Using BERT
    3m 41s
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    9. 
    BERT for Multiple Choice
    3m 30s
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    10. 
    Using BERT and Python for Multiple Choice
    4m 8s
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    11. 
    BERT for Token Classification
    2m 34s
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    12. 
    Implementing Token Classification Using BERT
    2m 49s
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    13. 
    BERT for Question Answering
    3m 26s
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    14. 
    Implementing Question-answering Machines Using BERT
    3m 7s
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    15. 
    Guidelines for Content Optimization Using Bert
    3m 6s
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    16. 
    Course Summary
    1m 12s

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