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Author Topic: Latest Machine Learning techniques applied to Morse decoding  (Read 390 times)
AG1LE
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« on: November 05, 2017, 06:36:02 PM »

I did some new Morse decoding experiments with Google's TensorFlow open-source software library for Machine Intelligence. I built a LSTM based Dynamic RNN model and trained it with some random text and tested against ARRL 20 WPM materials.  To see the latest results check out:
http://ag1le.blogspot.com/2017/11/tensorflow-revisited-new-lstm-dynamic.html

PS.  Source code is available in Github https://github.com/ag1le/LSTM_morse.
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AE8RS
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« Reply #1 on: November 06, 2017, 06:09:16 PM »

This looks really interesting. It's been on my list to look into integrating a CW decode engine into a monitoring station. I think this might be a really awesome start.
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N0PP
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« Reply #2 on: November 07, 2017, 06:30:32 AM »

Great work. I already work with Tensorflow in another field (image processing). I was planning on looking into CW decoding with deep learning for a while. This will get me started with my own experiments. Thanks for posting!
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