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
Instructor: Google Cloud Training
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4.1
(108 reviews)
Advanced level
Designed for those already in the industry
Flexible schedule
Approx. 1 hour
Learn at your own pace
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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.
Skills you'll gain
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Assessments
1 assignment
Taught in English
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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
Instructor ratings
4.3 (21 ratings)Offered by
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4.1
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Showing 3 of 108
WC
4
Reviewed on Mar 12, 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.
KB
4
Reviewed on Mar 21, 2025
Lab no longer works end to end. I was able to run until we started building classification model. TesnsorFlow code is no longer compiling.
NA
4
Reviewed on Jul 16, 2023
course was amazing gave me a good overview of BERT model and concepts like Encoding and decoding but not for beginner :>
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