Deep Dive into LLMs like ChatGPT – Course Introduction

Course Overview

This is an in-depth lecture on Large Language Model (LLM) AI technology designed for a general audience, focusing primarily on the technical principles underpinning ChatGPT and related products. The course comprehensively covers the entire training stack of model development, including how to understand the model's "psychological" reasoning framework and how to best utilize these models in practical applications.

Release Date: February 6, 2025
Views: 3,899,830

Instructor Introduction

Andrej Karpathy is an expert with extensive experience in the field of AI:

• Founding member of OpenAI (2015)
• Senior Director of AI at Tesla (2017–2022)
• Currently founder of Eureka Labs, building an AI-native school

Instructor’s Goal: To enhance public awareness and understanding of cutting-edge AI technologies and empower people to effectively leverage the latest and best AI tools in their work.

More Information:

• Personal Website: https://karpathy.ai/
• Twitter: https://x.com/karpathy

Course Outline

Fundamentals

00:00:00 Introduction

Course introduction

00:01:00 Pretraining Data (Internet)

Pretraining data (internet-sourced data)

00:07:47 Tokenization

Tokenization techniques

00:14:27 Neural Network I/O

Neural network input/output

00:20:11 Neural Network Internals

Internal structure of neural networks

00:26:01 Inference

Inference process

Model Training

00:31:09 GPT-2: Training and Inference

GPT-2: Training and inference

00:42:52 Llama 3.1 Base Model Inference

Llama 3.1 base model inference

00:59:23 Pretraining to Post-Training

From pretraining to post-training

01:01:06 Post-Training Data (Conversations)

Post-training data (conversation data)

Advanced Features

01:20:32 Hallucinations, Tool Use, Knowledge/Working Memory

Hallucinations, tool usage, knowledge/working memory

01:41:46 Knowledge of Self

Self-awareness

01:46:56 Models Need Tokens to Think

Models require tokens to think

02:01:11 Tokenization Revisited: Models Struggle with Spelling

Revisiting tokenization: models struggle with spelling

02:04:53 Jagged Intelligence

Jagged intelligence

Reinforcement Learning

02:07:28 Supervised Finetuning to Reinforcement Learning

From supervised fine-tuning to reinforcement learning

02:14:42 Reinforcement Learning

Reinforcement learning

02:27:47 DeepSeek-R1

DeepSeek-R1 model

02:42:07 AlphaGo

AlphaGo case study

02:48:26 Reinforcement Learning from Human Feedback (RLHF)

Reinforcement learning from human feedback (RLHF)

Conclusion

03:09:39 Preview of Things to Come

Preview of future developments

03:15:15 Keeping Track of LLMs

Tracking LLM progress

03:18:34 Where to Find LLMs

Where to find LLMs

03:21:46 Grand Summary

Comprehensive summary

Course Highlights

1. Comprehensive and Systematic: Covers the full LLM technology stack—from basic data preprocessing to advanced reinforcement learning.
2. Practice-Oriented: Explains not only theory but also practical application of these models.
3. Accessible Yet Technical: Designed for a general audience while maintaining technical depth.
4. Cutting-Edge Content: Includes the latest models such as Llama 3.1 and DeepSeek-R1.

Target Audience

• AI/machine learning beginners seeking a systematic understanding of LLM technology
• Developers aiming to deeply understand the underlying mechanisms of products like ChatGPT
• General audiences interested in AI technology
• Professionals who need to apply LLMs in their work

Learning Outcomes

Upon completing this course, you will be able to:

• Understand how large language models work
• Master the complete pipeline from data preprocessing to model deployment
• Learn how to effectively use LLMs to solve real-world problems
• Recognize the capabilities, boundaries, and limitations of LLMs
• Track and evaluate the latest developments in LLM technology