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Compiles as a single include with no external dependencies, relying solely on C++14 standard library features for tensor operations and memory management.
Distinct from Header-only Libraries: Distinct from Header-only Libraries: focuses specifically on header-only deep learning frameworks rather than generic header-only utility libraries.
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tiny-dnn is a header-only C++14 deep learning framework for building, training, and running inference on neural networks. It constructs static computational graphs at compile time using template-based layer composition, with a gradient-based backpropagation engine and minibatch stochastic gradient descent for training, all without external dependencies beyond the C++14 standard library. The framework supports importing pre-trained models from the Caffe framework directly, parsing its binary serialization format without requiring external protocol buffer libraries. It provides CPU-optimized te
Provides a header-only C++14 deep learning framework with no external dependencies.