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@@ -198,32 +198,7 @@ Press **Train** — the network trains in real time, showing:
* Accuracy
* Six random preview samples with predicted labels
---
## Why This Project Matters
This repository demonstrates:
* understanding of GPU buffer layouts
* compute pipeline dispatching
* custom WGSL kernels for ML
* manual backprop and gradient math
* GPU memory management and buffer resizing
* a full deep-learning training loop without any frameworks
It is strong evidence of skill in:
**AI systems engineering**,
**WebGPU compute design**,
**neural-network internals**,
and **performance-oriented GPU development**.
These skills are directly relevant for:
* AI inference runtimes
* GPU acceleration teams
* Web-based model execution
* systems-level optimization roles
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