AI Practitioner: Practical BERT Examples
Artificial Intelligence
| Expert
- 16 videos | 50m 7s
- Includes Assessment
- Earns a Badge
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
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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
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2m 36s
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3m 40sAfter completing this video, you will be able to name practical approaches to improving search using BERT. FREE ACCESS
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3. How BERT is Used in Searches2m 34sUpon completion of this video, you will be able to describe how BERT functions inside of a search engine. FREE ACCESS
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4. Implementing a Mini Search Engine Using BERT3m 28sIn this video, you will learn how BERT can be used to search the text of a given document. FREE ACCESS
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5. BERT for Next Sentence Prediction2m 51sAfter completing this video, you will be able to describe how we can use BERT for next sentence prediction. FREE ACCESS
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6. Using BERT and Python for Next Sentence Prediction3m 52sIn this video, you will learn how to use BERT and Python for next sentence prediction. FREE ACCESS
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7. BERT for Sequence Classification3m 35sIn this video, you will outline how BERT can be used for sequence classification. FREE ACCESS
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8. Implementing a Sequence Classifier Using BERT3m 41sIn this video, you will work with BERT to implement a sequence classifier. FREE ACCESS
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9. BERT for Multiple Choice3m 30sAfter completing this video, you will be able to describe how to do multiple-choice reading comprehension using BERT. FREE ACCESS
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10. Using BERT and Python for Multiple Choice4m 8sFind out how to use BERT and Python to implement multiple choice examples via a PyTorch implementation of BERT. FREE ACCESS
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11. BERT for Token Classification2m 34sIn this video, you will outline how to use BERT for token classification. FREE ACCESS
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12. Implementing Token Classification Using BERT2m 49sIn this video, you will learn how to work with BERT to implement a token classifier. FREE ACCESS
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13. BERT for Question Answering3m 26sAfter completing this video, you will be able to describe how to develop a question-answering machine using BERT. FREE ACCESS
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14. Implementing Question-answering Machines Using BERT3m 7sIn this video, you will work with BERT to implement a question-answering machine. FREE ACCESS
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15. Guidelines for Content Optimization Using Bert3m 6sFind out how to outline some fundamental guidelines for content optimization using BERT. FREE ACCESS
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16. Course Summary1m 12sIn this video, we will summarize the key concepts covered in this course. FREE ACCESS
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