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README update
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README.md
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README.md
@ -32,7 +32,7 @@ This gives 10 outputs to the neural network. The test program will output the
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accuracy for each digit. Expect above 99% accuracy for the correct digit, and
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less that 0.1% accuracy for the other digits.
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# Features
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## Features
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* Portable - Runs on Windows, MacOS, Linux, and embedded chips like ARM, AVR, and Microchip
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@ -40,7 +40,7 @@ less that 0.1% accuracy for the other digits.
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* One hidden layer.
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# Tips
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## Tips
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* Tinn will never use more than the C standard library.
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@ -60,7 +60,7 @@ single threaded to aid development for embedded systems.
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* Get greater training accuracy by annealing your learning rate. For instance, multiply
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your learning rate by 0.99 every training iteration. This will zero in on a good learning minima.
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# Disclaimer
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## Disclaimer
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Tinn is a practice in minimalism.
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@ -70,11 +70,11 @@ Tinn is not a fully featured neural network C library like Kann, or Genann:
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https://github.com/codeplea/genann
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# Ports
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## Ports
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Rust: https://github.com/dvdplm/rustinn
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# Other
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## Other
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[A Tutorial using Tinn NN and CTypes](https://medium.com/@cknorow/creating-a-python-interface-to-a-c-library-a-tutorial-using-tinn-nn-d935707dd225)
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