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DeepLearning.AI

Attention in Transformers: Concepts and Code in PyTorch

  • up to 1 hour
  • Beginner

This course provides a clear explanation of the attention mechanism in transformers, a breakthrough architecture powering large language models like ChatGPT. Learn how to code attention mechanisms in PyTorch and improve your understanding of AI applications.

  • Attention mechanism
  • Transformers
  • PyTorch
  • Self-attention
  • Masked self-attention

Overview

In this course, you will delve into the attention mechanism, a key component of transformers, and learn how to implement it using PyTorch. You'll explore the relationships between word embeddings, positional embeddings, and attention, and understand the roles of Query, Key, and Value matrices. The course covers self-attention, masked self-attention, and cross-attention, providing a comprehensive understanding of how these concepts are incorporated into transformers. By the end, you'll be equipped with the knowledge to build reliable and scalable AI applications.

  • Web Streamline Icon: https://streamlinehq.com
    Online
    course location
  • Layers 1 Streamline Icon: https://streamlinehq.com
    English
    course language
  • Self-paced
    course format
  • Live classes
    delivered online

Who is this course for?

Python Enthusiasts

Individuals with basic Python knowledge interested in learning about the attention mechanism in LLMs.

AI Developers

Developers looking to understand the foundational architecture of transformers to build scalable AI applications.

Data Scientists

Data scientists aiming to enhance their understanding of attention mechanisms in large language models.

This course offers a deep dive into the attention mechanism, a crucial component of transformers, enabling learners to understand and implement it using PyTorch. Ideal for beginners and professionals, it provides the skills needed to advance in AI and machine learning.

Pre-Requisites

1 / 3

  • Basic knowledge of Python

  • Interest in AI and machine learning

  • Understanding of basic mathematical concepts

What will you learn?

Introduction
An overview of the course and its objectives.
The Main Ideas Behind Transformers and Attention
Exploration of the core concepts of transformers and the attention mechanism.
The Matrix Math for Calculating Self-Attention
Detailed explanation of the mathematical calculations involved in self-attention.
Coding Self-Attention in PyTorch
Practical coding session to implement self-attention using PyTorch.
Self-Attention vs Masked Self-Attention
Comparison between self-attention and masked self-attention, highlighting their differences and uses.
The Matrix Math for Calculating Masked Self-Attention
Mathematical breakdown of masked self-attention calculations.
Coding Masked Self-Attention in PyTorch
Hands-on coding session to implement masked self-attention in PyTorch.
Encoder-Decoder Attention
Understanding the role of attention in the encoder-decoder architecture.
Multi-Head Attention
Exploration of multi-head attention and its significance in transformers.
Coding Encoder-Decoder Attention and Multi-Head Attention in PyTorch
Practical coding session to implement encoder-decoder and multi-head attention using PyTorch.
Conclusion
Summary of the course and key takeaways.
Quiz
Assessment to test understanding of the course material.
Appendix – Tips and Help
Additional resources and tips for further learning.

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