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.
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This course is estimated to take approximately 45 minutes to complete.
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.
Syllabus
Transformer Models and BERT Model: Overview
Module 1
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.
