DeepLearning.AI

Josh Starmer

Dive deep into the attention mechanism that revolutionized AI with transformers. This course, taught by Josh Starmer, covers the evolution of attention, the role of Query, Key, and Value matrices, and the differences between self-attention and masked self-attention. Gain hands-on experience coding these concepts in PyTorch, and understand how they contribute to building scalable AI applications.

Attention in Transformers: Concepts and Code in PyTorch

An overview of the course and its objectives.

Introduction

Exploration of the core concepts of transformers and the attention mechanism.

The Main Ideas Behind Transformers and Attention

Detailed explanation of the mathematical foundations for self-attention.

The Matrix Math for Calculating Self-Attention

Hands-on coding session to implement self-attention using PyTorch.

Coding Self-Attention in PyTorch

Comparison between self-attention and masked self-attention mechanisms.

Self-Attention vs Masked Self-Attention

Mathematical insights into masked self-attention calculations.

The Matrix Math for Calculating Masked Self-Attention

Practical coding exercise to implement masked self-attention in PyTorch.

Coding Masked Self-Attention in PyTorch

Understanding the role of attention in encoder-decoder architectures.

Encoder-Decoder Attention

Exploration of multi-head attention and its benefits in transformers.

Multi-Head Attention

Coding session to implement encoder-decoder and multi-head attention using PyTorch.

Coding Encoder-Decoder Attention and Multi-Head Attention in PyTorch

Conclusion

Assessment to test understanding of the course material.

Quiz

Additional resources and tips for further learning.

Appendix – Tips and Help

This course provides a comprehensive understanding of the attention mechanism in transformers, a key component in large language models like ChatGPT. Learn to code attention mechanisms in PyTorch and enhance your AI application development skills.

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

Python Enthusiasts

Developers looking to enhance their understanding of transformers and attention mechanisms in AI applications.

AI Developers

Data scientists aiming to improve their skills in building scalable AI models using PyTorch.

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Attention in Transformers: Concepts and Code in PyTorch

Overview

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