I’m experimenting with whether implementing a small neural network engine can be a good teaching artifact for intermediate Rust concepts.
The project intentionally avoids external crates and focuses on:
- flat memory layouts for tensors
- explicit indexing math
- simple, readable implementations over abstractions
The ML side exists mainly to force concrete design tradeoffs.
I’d appreciate feedback specifically on:
• API boundaries
• ownership/borrowing choices
• where this stops being idiomatic Rust
• whether this approach clarifies or obscures Rust concepts
Guide + code walkthrough: