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
Gain insight into a topic and learn the fundamentals.
125 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 assignment
Taught in English
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There is 1 module in this course
Instructor
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Showing 3 of 125
KL
Reviewed on Oct 28, 2023
I need to use my on GCP to run the lab. Otherwise, very good introduction to get going on Transformers
WC
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.
NK
Reviewed on May 13, 2025
I am looking for free resources for experimentation, or a lighter model that can run on my laptop.
