tiny-dnn is a C++11 implementation of deep learning

  • reasonably fast, without GPU
    • with TBB threading and SSE/AVX vectorization
    • 98.8% accuracy on MNIST in 13 minutes training (@Core i7-3520M)
  • portable & header-only
    • Run anywhere as long as you have a compiler which supports C++11
    • Just include tiny_dnn.h and write your model in C++. There is nothing to install.
  • easy to integrate with real applications
    • no output to stdout/stderr
    • a constant throughput (simple parallelization model, no garbage collection)
    • work without throwing an exception
    • can import caffe’s model
  • simply implemented
    • be a good library for learning neural networks

https://github.com/tiny-dnn/tiny-dnn#examples

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