Shypd.aiThe largest open index of AI toolsKANN is a lightweight C library for artificial neural networks, supporting multi-layer perceptrons, convolutional neural networks, and recurrent neural networks. It implements graph-based reverse-mode automatic differentiation for building complex neural networks.
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KANN is a standalone and lightweight C library designed for constructing and training small to medium artificial neural networks. It supports various architectures including multi-layer perceptrons, convolutional neural networks, and recurrent neural networks (LSTM and GRU). The library implements graph-based reverse-mode automatic differentiation, enabling the creation of topologically complex neural networks with features like recurrence, shared weights, and multiple inputs/outputs/costs. Unlike mainstream deep learning frameworks, KANN prioritizes a smaller codebase and minimal dependencies, making it suitable for C/C++ experimentation, deploying moderately sized models without dependency issues, or learning deep learning library internals. It offers flexibility in model construction, efficient matrix operations, and portability with less than 4000 lines of code.
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