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.
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Transformer Models and BERT Model
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Instructor: Google Cloud Training
Access provided by Coursera Learning Team
8,966 already enrolled
(94 reviews)
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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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 assignment
Instructor
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Learner reviews
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Reviewed on Mar 13, 2024
Excellent and concise presentation of Transformer and BERT models. The course designer may consider adding programming assignments to illustrate the concepts and to reinforce student learning.
Reviewed on Jan 31, 2025
Concise, challenging, thought-provoking. This course is an immersive look into the inner workings of Transformer models, and the BERT model
Reviewed on Oct 29, 2023
I need to use my on GCP to run the lab. Otherwise, very good introduction to get going on Transformers
Recommended if you're interested in Information Technology
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