Building makemore Part 2: MLP

# AI Training Script: Building makemore Part 2: MLP

## Overview
• MLP language model implementation
• Follows Bengio 2003 paper
• Training/dev/test splits for LMs
• Embeddings and hidden layers
• Step up from bigram to MLP

**Best for:** ML engineers following Karpathy's zero-to-hero series  
**Category:** LLM Fundamentals | **Difficulty:** Advanced | **Signal Score:** 97/100

## Training Objective
After studying this content, an agent should be able to: **Build an MLP-based language model following seminal Bengio 2003 architecture**

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

## Key Tools & Technologies
• PyTorch
• MLP
• Language Models
• Python

## Key Learning Points
• MLP language model implementation
• Follows Bengio 2003 paper
• Training/dev/test splits for LMs
• Embeddings and hidden layers
• Step up from bigram to MLP

## Implementation Steps
[ ] Watch full video
[ ] Setup: PyTorch, MLP, Language Models, Python
[ ] Implement
[ ] Test
[ ] Document

## Agent Execution Prompt
Study this llm fundamentals video and implement the key concepts.

## 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, mlp, language models, 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]