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:

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