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

# AI Training Script: Let's build GPT: from scratch, in code, spelled out.

## Overview
• Builds GPT from scratch in PyTorch
• Implements self-attention mechanism step-by-step
• Covers positional encoding and layer normalization
• Trains a character-level language model live
• Deep understanding of transformer internals

**Best for:** ML engineers wanting to understand GPT internals from the ground up  
**Category:** LLM Fundamentals | **Difficulty:** Advanced | **Signal Score:** 99/100

## Training Objective
After studying this content, an agent should be able to: **Implement a transformer-based language model from scratch**

## Prerequisites
• Strong background in LLM Fundamentals
• Production experience recommended
• Deep familiarity with: PyTorch

## Key Tools & Technologies
• PyTorch
• GPT
• Transformers
• Python

## Key Learning Points
• Builds GPT from scratch in PyTorch
• Implements self-attention mechanism step-by-step
• Covers positional encoding and layer normalization
• Trains a character-level language model live
• Deep understanding of transformer internals

## Implementation Steps
[ ] Study the full tutorial
[ ] Identify the main tools: PyTorch, GPT, Transformers, Python
[ ] Implement: Implement a transformer-based language model from scratch
[ ] Test with a real example
[ ] Document what you learned

## Agent Execution Prompt
Watch this video about llm fundamentals and implement the key techniques demonstrated.

## Success Criteria
An agent completing this training should be able to:
- Explain the core concepts covered in this tutorial
- Execute the demonstrated workflow with PyTorch
- Troubleshoot common issues at the advanced level
- Apply the technique to similar real-world scenarios

## Topic Tags
pytorch, gpt, transformers, python, llm-fundamentals, advanced

## Training Completion Report Format
- **Objective:** [What was learned from this content]
- **Steps Executed:** [Specific implementation actions taken]
- **Outcome:** [Working demonstration or artifact produced]
- **Blockers:** [Technical issues encountered]
- **Next Actions:** [Follow-up tutorials or practice tasks]