2 Repos
Awesome GitHub RepositoriesHeader-Only Implementations
A deep learning framework implemented entirely in header files with no external dependencies, supporting feedforward networks and CPU training.
Distinct from Deep Learning Framework Implementations: Distinct from Deep Learning Framework Implementations: focuses specifically on header-only implementations rather than general deep learning framework implementations.
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Awesome Header-Only Implementations GitHub Repositories
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- tiny-dnn/tiny-dnn
tiny-dnn/tiny-dnn
6,019Auf GitHub ansehentiny-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.
C++ - floooh/chips
floooh/chips
1,177Auf GitHub ansehenChips is a modular, header-only C library framework designed for building cycle-accurate hardware simulators and replicating historical eight-bit computing architectures. It provides the foundational components necessary to construct complete vintage systems by integrating individual emulated microprocessors and peripheral chips. The framework distinguishes itself through a component-based architecture where hardware modules are implemented as standalone headers that can be wired together to form complex systems. It models hardware interactions at a low level, utilizing pin-level signal emula
Implements hardware modules as standalone C headers that can be wired together to form complex system architectures.
C
