Transformers, explained: Understand the model behind ChatGPT

🚀 Learn AI Prompt Engineering: https://bit.ly/3v8O4Vt

In this technical overview, we dissect the architecture of Generative Pre-trained Transformer (GPT) models, drawing parallels between artificial neural networks and the human brain.

From the foundational GPT-1 to the advanced GPT-4, we explore the evolution of GPT models, focusing on their learning processes, the significance of data in training, and the revolutionary Transformer architecture.

This video is designed for curious non-technical people looking to understand the complexities of GPT models in a way that's easy to understand.

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⏰ Timestamps:

0:00 - Intro
0:27 - The Importance of Modeling The Human Brain
1:10 - Basics of Artificial Neural Networks (ANNs)
2:26 - Overview of GPT Models Evolution
3:34 - Training Large Language Models
7:05 - Transformer Architecture
7:45 - Understanding Tokenization
10:19 - Explaining Token Embeddings
17:03 - Deep Dive into Self-Attention Mechanism
18:53 - Multiheaded Self-Attention Explained
19:55 - Predicting the Next Word: The Process
22:33 - De-Tokenization: Converting Token IDs Back to Words

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