Let's build GPT: from scratch, in code, spelled out.

This podcast episode explores the development and implementation of ChatGPT, a powerful AI system based on the transformer architecture. The episode covers topics such as the Tiny Shakespeare dataset used for training, language modeling, tokenization strategies, and the process of re-representing a dataset. It also delves into the implementation of the bigram language model, model generation, and the use of self-attention in transformers. The section discusses the significance of self-attention, residual connections, and layer norm in optimizing deep neural networks. The implementation of a decoder-only transformer model, triangular masks in transformers for language modeling, and the training process of ChatGPT are also covered.

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Andrej Karpathy

Introducing ChatGPT and the Transformer Architecture

Training a transformer neural network on the Tiny Shakespeare dataset

Introduction to Language Modeling and Tokenization Strategies

The importance of separating the dataset and training the transformer

Predictions and Loss Calculation in the Bigram Language Model

Explanation of Model Generation and Training

Developing a Bigram Model with Optimizer AdamW

Introduction to Self-Attention and Efficient Implementation in Transformers

Efficient Calculation Using Matrix Multiplication

Weighted Aggregations and Self-Attention Block in Neural Networks

Understanding Self-Attention and Implementing a Single Individual Head

Implementing Self-Attention for Data-Dependent Information Gathering

Understanding Self-Attention Mechanism and Communication in Directed Graphs

Self-Attention and Cross-Attention in Transformer Models

The Implementation of Self-Attention and Scaled Attention in Neural Networks

Implementation of Self-Attention and Multi-Head Attention in the Transformer Network

The Importance of Self-Attention and Residual Connections in Optimizing Neural Networks

Implementing Layer Norm in Transformer Architecture and Scaling the Model

Scaling Up the Model: Improving Performance with Dropout Regularization

Understanding Transformers and the Role of Triangular Masks in Language Modeling

Training a ChatGPT: Pre-training vs Fine-tuning and the Stages Involved

Let's build GPT: from scratch, in code, spelled out.

Andrej Karpathy

00:00Introducing ChatGPT and the Transformer Architecture

Introducing ChatGPT and the Transformer Architecture

04:01Training a transformer neural network on the Tiny Shakespeare dataset

Training a transformer neural network on the Tiny Shakespeare dataset

08:14Introduction to Language Modeling and Tokenization Strategies

Introduction to Language Modeling and Tokenization Strategies

13:42The importance of separating the dataset and training the transformer

The importance of separating the dataset and training the transformer

21:53Predictions and Loss Calculation in the Bigram Language Model

Predictions and Loss Calculation in the Bigram Language Model

29:01Explanation of Model Generation and Training

Explanation of Model Generation and Training

34:50Developing a Bigram Model with Optimizer AdamW

Developing a Bigram Model with Optimizer AdamW

40:44Introduction to Self-Attention and Efficient Implementation in Transformers

Introduction to Self-Attention and Efficient Implementation in Transformers

46:55Efficient Calculation Using Matrix Multiplication

Efficient Calculation Using Matrix Multiplication

52:27Weighted Aggregations and Self-Attention Block in Neural Networks

Weighted Aggregations and Self-Attention Block in Neural Networks

58:09Understanding Self-Attention and Implementing a Single Individual Head

Understanding Self-Attention and Implementing a Single Individual Head

1:02:28Implementing Self-Attention for Data-Dependent Information Gathering

Implementing Self-Attention for Data-Dependent Information Gathering

1:07:59Understanding Self-Attention Mechanism and Communication in Directed Graphs

Understanding Self-Attention Mechanism and Communication in Directed Graphs

1:12:47Self-Attention and Cross-Attention in Transformer Models

Self-Attention and Cross-Attention in Transformer Models

1:17:03The Implementation of Self-Attention and Scaled Attention in Neural Networks

The Implementation of Self-Attention and Scaled Attention in Neural Networks

1:21:49Implementation of Self-Attention and Multi-Head Attention in the Transformer Network

Implementation of Self-Attention and Multi-Head Attention in the Transformer Network

1:26:32The Importance of Self-Attention and Residual Connections in Optimizing Neural Networks

The Importance of Self-Attention and Residual Connections in Optimizing Neural Networks

1:32:33Implementing Layer Norm in Transformer Architecture and Scaling the Model

Implementing Layer Norm in Transformer Architecture and Scaling the Model

1:37:49Scaling Up the Model: Improving Performance with Dropout Regularization

Scaling Up the Model: Improving Performance with Dropout Regularization

1:43:36Understanding Transformers and the Role of Triangular Masks in Language Modeling

Understanding Transformers and the Role of Triangular Masks in Language Modeling

1:49:22Training a ChatGPT: Pre-training vs Fine-tuning and the Stages Involved

Training a ChatGPT: Pre-training vs Fine-tuning and the Stages Involved