RustNN is a Rust-based neural network library built on top of the powerful ndarray library. It provides a straightforward implementation of classic neural network architectures, making it ideal for learning, experimentation, and developing a deeper understanding of how neural networks function under the hood.
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Easy-to-use neural network layers and operations
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Built on ndarray, providing efficient numerical computations
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Ideal for learning and educational purposes
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Minimal dependencies, leveraging the power of Rust’s ownership model and performance
RustNN was created as a personal project by a senior data science student aiming to:
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Develop proficiency in Rust programming
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Gain an in-depth understanding of neural networks by building them from scratch
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Showcase practical Rust coding skills and neural network knowledge
Disclaimer:
This project is inspired by Neural Networks from Scratch by Harrison Kinsley & Daniel Kukieła. The implementation is written in Rust and follows the concepts and structure from the book, but all code is original and adapted for Rust.