This course introduces you to the Transformer architecture and the Bidirectional Encoder Representations from Transformers (BERT) model. You learn about the main components of the Transformer architecture, such as the self-attention mechanism, and how it is used to build the BERT model. You also learn about the different tasks that BERT can be used for, such as text classification, question answering, and natural language inference. This course is estimated to take approximately 45 minutes to complete.
Transformer Models and BERT Model
Transformer Models and BERT Model
Taught in English
4,617 already enrolled
3.9
(38 reviews)
Advanced level
Designed for those already in the industry
1 hour to complete
Flexible schedule
Learn at your own pace
What you'll learn
Understand the main components of the Transformer architecture.
Learn how a BERT model is built using Transformers.
Use BERT to solve different natural language processing (NLP) tasks.
Details to know
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Assessments
1 quiz
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There is 1 module in this course
In this module you will learn about the main components of the Transformer architecture, such as the self-attention mechanism, and how it is used to build the BERT model. You also learn about the different tasks that BERT can be used for, such as text classification, question answering, and natural language inference.
What's included
2 videos1 reading1 quiz
Instructor
Instructor ratings
4.6 (6 ratings)
Offered by
Recommended if you're interested in Cloud Computing
Google Cloud
Google Cloud
Google Cloud
Google Cloud
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