RustNN

The library offers an intuitive interface with methods like `train()` and configurable options, as shown in the XOR example, making it easy to set up and train networks without boilerplate code.

Built in Rust, it leverages the language's memory safety and performance benefits, ideal for systems where reliability and efficiency are priorities in neural network implementations.

Allows fine-tuning of learning rate, momentum, and halt conditions via the Trainer struct, providing granular control over the training process, as evidenced in the documented methods.

Supports training on data incrementally, which is useful for online learning scenarios, mentioned in the library description as a key feature for flexible model updates.