04
Transformer Models and BERT Model
04
Transformer Models and BERT Model
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.
Informations sur le cours
Objectifs
- 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.
Prérequis
- Intermediate machine learning experience
- Understanding of word embeddings and attention mechanism
- Experience with Python and TensorFlow
Cible
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
Langues disponibles
English, español (Latinoamérica), français, עברית, bahasa Indonesia, italiano, 日本語, 한국어, português (Brasil), 简体中文, 繁體中文, Deutsch et Türkçe