LLM Fundamentals
Advanced
Signal 96/100
Building makemore Part 3: Activations & Gradients, BatchNorm
by Andrej Karpathy
Teaches AI agents to
Diagnose and fix training instability using gradient visualization and batch normalization
Key Takeaways
- Makemore part 3: activations, gradients, BatchNorm
- Diagnosing and fixing training issues
- Visualizing gradient flow
- BatchNorm intuition and implementation
- Practical debugging of neural network training
Full Training Script
# AI Training Script: Building makemore Part 3: Activations & Gradients, BatchNorm ## Overview • Makemore part 3: activations, gradients, BatchNorm • Diagnosing and fixing training issues • Visualizing gradient flow • BatchNorm intuition and implementation • Practical debugging of neural network training **Best for:** ML engineers debugging neural network training instability **Category:** LLM Fundamentals | **Difficulty:** Advanced | **Signal Score:** 96/100 ## Training Objective After studying this content, an agent should be able to: **Diagnose and fix training instability using gradient visualization and batch normalization** ## Prerequisites • Strong background in LLM Fundamentals • Production experience recommended • Deep familiarity with: PyTorch ## Key Tools & Technologies • PyTorch • BatchNorm • Gradient Analysis • Neural Networks ## Key Learning Points • Makemore part 3: activations, gradients, BatchNorm • Diagnosing and fixing training issues • Visualizing gradient flow • BatchNorm intuition and implementation • Practical debugging of neural network training ## Implementation Steps [ ] Watch video [ ] Set up: PyTorch, BatchNorm, Gradient Analysis, Neural Networks [ ] Implement [ ] Test [ ] Document ## Agent Execution Prompt Implement the key llm fundamentals 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 advanced level - Apply the technique to similar real-world scenarios ## Topic Tags pytorch, batchnorm, gradient analysis, neural networks, 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]
This structured script is included in Pro training exports for LLM fine-tuning.
Execution Checklist
[ ] Watch video [ ] Set up: PyTorch, BatchNorm, Gradient Analysis, Neural Networks [ ] Implement [ ] Test [ ] Document
