Lecture 5: How does GPT-3 really work?

In this lecture, we will understand how GPT-3 works. We will start by looking at the history of GPT: from transformers to GPT to GPT-2 to GPT-3 and then to GPT-4. Then we will discuss about Zero-shot and Few-shot learning. We will discuss the autoregressive and unsupervised nature of GPT pre-training in detail. We end the lecture with a note on the emergent behavior shown by language models.

The key reference book which this video series very closely follows is Build a Large Language Model from Scratch by Manning Publications. All schematics and their descriptions are borrowed from this incredible book!

This book serves as a comprehensive guide to understanding and building large language models, covering key concepts, techniques, and implementations.

Affiliate links for purchasing the book will be added soon. Stay tuned for updates!

0:00 Introduction and recap
1:20 Transformers, GPT, GPT-2, GPT-3 and GPT-4
9:50 Zero Shot vs Few Shot learning
18:18 Datasets for GPT pre-training
27:16 Next word prediction
38:11 Emergent behaviour
42:19 Recap of lecture

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