neurox 0.2.0

A Rust crate providing core building blocks for numerical computing and machine learning, including tensor operations, model abstractions, and future GPU acceleration support.
Documentation

Neurox

Overview

Neurox is a fast, minimalist, and extendable numerical computation & machine learning library written in Rust.
It provides tensor operations, activation functions, layer abstractions, and model building blocks to create and run ML models.
Currently optimized for CPU execution, with a GPU backend planned in future releases.

Perfect for:

  • Learning how ML frameworks work under the hood
  • Building lightweight ML models in Rust
  • Using as a base for larger GPU-accelerated projects

Features (v0.2.0)

  • Multi-dimensional Tensor struct for efficient numerical storage
  • Matrix operations: multiplication, addition, dot products
  • Activation functions: ReLU, Sigmoid, Tanh, Softmax, LeakyReLU, XOR-like logical ops
  • Layer system: Dense layers with bias & activation support
  • Model API: Create, add layers, run forward passes
  • Logical / Boolean operations on tensors (e.g., XOR, AND, OR)
  • Random initialization utilities
  • Device abstraction for future GPU acceleration
  • Modular architecture extend with custom layers or activations easily
  • Example scripts for quick usage

📦 Installation

Add Neurox to your Rust project:

[dependencies]
neurox = "0.2.0"