04
Transformer Models and BERT Model
04
Transformer Models and BERT Model
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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.
Course Info
Objectives
- 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.
Prerequisites
- Intermediate machine learning experience
- Understanding of word embeddings and attention mechanism
- Experience with Python and TensorFlow
Audience
This course is intended for those who are interested in learning about text classification, question answering, and natural language inference, such as:
- Data scientists
- Machine learning engineers
- Software engineers
Available languages
English, español (Latinoamérica), français, עברית, bahasa Indonesia, italiano, 日本語, 한국어, português (Brasil), 简体中文, 繁體中文, Deutsch, and Türkçe
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Interested in taking this course with one of our authorized on-demand partners?
Explore Google Cloud content on Coursera and Pluralsight.
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View the public classroom schedule here.
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