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Awesome GitHub RepositoriesCPU-Optimized Training
Trains feedforward and convolutional networks on CPU using minibatch SGD with configurable optimizers and loss functions.
Distinct from Convolutional Neural Network Training: Distinct from Convolutional Neural Network Training: focuses on CPU-optimized training specifically, not just CNN training in general.
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Awesome CPU-Optimized Training GitHub Repositories
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- tiny-dnn/tiny-dnn
tiny-dnn/tiny-dnn
6,019Ver en GitHubtiny-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
Trains feedforward and convolutional networks on CPU using minibatch SGD with configurable optimizers.
C++
