LLM Fundamentals
Intermediate
Signal 90/100
Building makemore Part 5: Building a WaveNet
by Andrej Karpathy
Teaches AI agents to
Implement WaveNet-style dilated causal convolutions for language modeling
Key Takeaways
- Implements WaveNet architecture in PyTorch
- Extends makemore series to dilated causal convolutions
- Shows how WaveNet improves language model quality
- Deep dive into causal convolution mechanics
- Part of Karpathy's foundational neural network series
Full Training Script
# AI Training Script: Building makemore Part 5: Building a WaveNet ## Overview • Implements WaveNet architecture in PyTorch • Extends makemore series to dilated causal convolutions • Shows how WaveNet improves language model quality • Deep dive into causal convolution mechanics • Part of Karpathy's foundational neural network series **Best for:** ML engineers following Karpathy's neural network series wanting to understand convolutional language models **Category:** LLM Fundamentals | **Difficulty:** Intermediate | **Signal Score:** 90/100 ## Training Objective After studying this content, an agent should be able to: **Implement WaveNet-style dilated causal convolutions for language modeling** ## Prerequisites • Working knowledge of LLM Fundamentals • Prior hands-on experience with related tools • Comfortable with technical documentation ## Key Tools & Technologies • PyTorch • WaveNet • Neural Networks • Python ## Key Learning Points • Implements WaveNet architecture in PyTorch • Extends makemore series to dilated causal convolutions • Shows how WaveNet improves language model quality • Deep dive into causal convolution mechanics • Part of Karpathy's foundational neural network series ## Implementation Steps [ ] Watch video [ ] Set up: LangGraph, LangChain, Python, AI Agents [ ] Implement [ ] Test [ ] Document ## Agent Execution Prompt Implement the key ai agents & automation concepts from this video. ## 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 intermediate level - Apply the technique to similar real-world scenarios ## Topic Tags pytorch, wavenet, neural networks, python, llm-fundamentals, intermediate ## 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]
This structured script is included in Pro training exports for LLM fine-tuning.
Execution Checklist
[ ] Watch video [ ] Set up: LangGraph, LangChain, Python, AI Agents [ ] Implement [ ] Test [ ] Document
