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README update

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Gustav Louw 2018-08-13 07:16:06 -07:00
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@ -32,7 +32,7 @@ This gives 10 outputs to the neural network. The test program will output the
accuracy for each digit. Expect above 99% accuracy for the correct digit, and
less that 0.1% accuracy for the other digits.
# Features
## Features
* Portable - Runs on Windows, MacOS, Linux, and embedded chips like ARM, AVR, and Microchip
@ -40,7 +40,7 @@ less that 0.1% accuracy for the other digits.
* One hidden layer.
# Tips
## Tips
* Tinn will never use more than the C standard library.
@ -60,7 +60,7 @@ single threaded to aid development for embedded systems.
* Get greater training accuracy by annealing your learning rate. For instance, multiply
your learning rate by 0.99 every training iteration. This will zero in on a good learning minima.
# Disclaimer
## Disclaimer
Tinn is a practice in minimalism.
@ -70,11 +70,11 @@ Tinn is not a fully featured neural network C library like Kann, or Genann:
https://github.com/codeplea/genann
# Ports
## Ports
Rust: https://github.com/dvdplm/rustinn
# Other
## Other
[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)