Overview

This course introduces you to the Transformer architecture and the Bidirectional Encoder Representations from Transformers (BERT) model. 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. Additionally, you will explore the different tasks that BERT can be used for, including text classification, question answering, and natural language inference.

Prerequisites

• Intermediate machine learning experience
• Understanding of word embeddings and attention mechanism
• Experience with Python and TensorFlow

Modules

• Machine Learning Model Training
• Encoder-Decoder Architecture
• Machine Learning

Price

No cost

Discounts

This course is estimated to take approximately 45 minutes to complete.

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