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add technical documentation
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about.adoc

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= About
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jrf g
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:revnumber: 1
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:revdate: {localmonth} {localday}, {localyear}
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:revremark: First Edition
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:description: A nice alternative for people who hate READMEs
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:experimental:
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:!toc:
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NOTE: This guide is for KDE linux (ideally on a ROG Maximus motherboard), however other OSes are supported
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== Download
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. go to GitHub
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. find the Repo
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. download with menu:Code[ZIP]
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. unzip with menu:Folder[Unzip] in Dolphin at menu:KDE[Dolphin]
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[sidebar]
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****
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Try Now!
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****
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== Installation after Download
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. Install the requirements
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. Install CoffeeScript
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. Install NodeJS 4.1 or preferably newer
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WARNING: Attempting to install the CUDA edition of PyTorch or having it already installed has a small chance of damaging the processor.
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This damage will especially occur if you edit the device variable in the code.
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Dont install it and if already installed, uninstall it.
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CAUTION: Make sure not to install NodeJS 4.0 or older
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it may result in missing built-in dependencies
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== Setup
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. Insert classify.png
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. Insert training data in directory
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== Execution
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Run executer.sh with menu:File[Open > Open in Terminal]
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or, in the terminal, run the following
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[source,bash]
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----
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python optimizedcputrainer.py
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python classifyandpredict.py
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----
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TIP: Always run executer.sh from the project directory
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but the script gives to 10 seconds to cancel if not running from the directory so you can always cancel with kbd:[Ctrl+C] until athe countdown finishes
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== Aftermath
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CAUTION: it is advised check the following before treating and/or claiming the output as "real information"
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* Confidence Score
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* Confusion Matrix
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NOTE: if the confidence is high, you are ready to treat it as real information
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with precaution as long as you tell anyone you share it with that it was a generated
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IMPORTANT: If you ignored the aforementioned warning
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there may be minor damage to the processor if there is
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press btn:[UEFI BIOS] at boot and go to menu:Exit[Reset] then press btn:[confirm]
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== License
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see license.txt

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