Free course materials which are based on NeuronRain OpenSource Codebase are committed in course_material/NeuronRain.

They have been segregated into:

(*) NeuronRain - Advanced Computer Science and Machine Learning (Graduate and Doctoral)

./NeuronRain/AdvancedComputerScienceAndMachineLearning/AdvancedComputerScienceAndMachineLearning.txt

(*) NeuronRain - Linux Kernel and Cloud (for students and Linux Kernel/Cloud professionals)

./NeuronRain/LinuxKernelAndCloud/LinuxKernelAndCloud.txt

(*) NeuronRain - Cloud and Bigdata Analytics (for students and Cloud Data science professionals)

./NeuronRain/LinuxKernelAndCloud/BigdataAnalyticsCloud_CourseNotes.txt

Generic Programming and Computer Science Miscellany course materials are in course_material/Programming and course_material/ComputerScienceMiscellaneous:

(*) Programming - C/C++/Java/Python/GUI (for students and Professionals)

./Programming/Python/Python_CourseNotes.txt ./Programming/Java/Java_CourseNotes.txt ./Programming/C/C_CourseNotes.txt ./Programming/R/R_CourseNotes.txt ./Programming/C++/CPlusPlus_CourseNotes.txt ./Programming/GUI/

(*) Miscellaneous Computer Science (Undergraduate Computer Science)

./ComputerScienceMiscellaneous/ComputerScienceMiscellaneous_CourseNotes.txt

These will be updated periodically adapted for classroom teaching depending on feasibility of free courses. Puzzles and Questions are sourced from various textbooks, competitive examinations, bigdata usecases and refer to copyrighted materials - author's publications and yet to be published drafts on various computer science topics.     NeuronRain Documentation: http://neuronrain-documentation.readthedocs.io/en/latest/

Apart from the above code examples and texts in GRAFIT repositories, following NeuronRain Design Texts spread across SourceForge,GitHub and GitLab repos constitute the main GRAFIT course materials which are more frequently updated commentaries on code commits to respective NeuronRain repositories accompanied by theory:

AsFer - https://github.com/shrinivaasanka/asfer-github-code/blob/master/asfer-docs/AstroInferDesign.txt

USBmd - https://github.com/shrinivaasanka/usb-md-github-code/blob/master/USBmd_notes.txt

USBmd64 - https://github.com/shrinivaasanka/usb-md64-github-code/blob/master/USBmd_notes.txt

VIRGO Linux - https://github.com/shrinivaasanka/virgo-linux-github-code/blob/master/virgo-docs/VirgoDesign.txt

VIRGO64 Linux - https://github.com/shrinivaasanka/virgo64-linux-github-code/blob/master/virgo-docs/VirgoDesign.txt

KingCobra - https://github.com/shrinivaasanka/kingcobra-github-code/blob/master/KingCobraDesignNotes.txt

KingCobra64 - https://github.com/shrinivaasanka/kingcobra64-github-code/blob/master/KingCobraDesignNotes.txt

AsFer - https://sourceforge.net/p/asfer/code/HEAD/tree/asfer-docs/AstroInferDesign.txt

USBmd - https://sourceforge.net/p/usb-md/code-0/HEAD/tree/USBmd_notes.txt

USBmd64 - https://sourceforge.net/p/usb-md64/code/ci/master/tree/USBmd_notes.txt

VIRGO Linux - https://sourceforge.net/p/virgo-linux/code-0/HEAD/tree/trunk/virgo-docs/VirgoDesign.txt

VIRGO64 Linux - https://sourceforge.net/p/virgo64-linux/code/ci/master/tree/virgo-docs/VirgoDesign.txt

KingCobra - https://sourceforge.net/p/kcobra/code-svn/HEAD/tree/KingCobraDesignNotes.txt

KingCobra64 - https://sourceforge.net/p/kcobra64/code/ci/master/tree/KingCobraDesignNotes.txt

AsFer - https://gitlab.com/shrinivaasanka/asfer-github-code/blob/master/asfer-docs/AstroInferDesign.txt

USBmd - https://gitlab.com/shrinivaasanka/usb-md-github-code/blob/master/USBmd_notes.txt

USBmd64 - https://gitlab.com/shrinivaasanka/usb-md64-github-code/blob/master/USBmd_notes.txt

VIRGO Linux - https://gitlab.com/shrinivaasanka/virgo-linux-github-code/blob/master/virgo-docs/VirgoDesign.txt

VIRGO64 Linux - https://gitlab.com/shrinivaasanka/virgo64-linux-github-code/blob/master/virgo-docs/VirgoDesign.txt

KingCobra - https://gitlab.com/shrinivaasanka/kingcobra-github-code/blob/master/KingCobraDesignNotes.txt

KingCobra64 - https://gitlab.com/shrinivaasanka/kingcobra64-github-code/blob/master/KingCobraDesignNotes.txt

Course notes in GRAFIT are non-linearly written than bottom-up or top-down textbook style of teaching. This is an experimental pedagogy based on following monte carlo simulation: () Concepts/topics are vertices of a universal graph of concepts/topics. () By random sampling and exposition of concept vertices, related neighbouring concepts are also touched. (*) Eventually high percentage of Topics in the Graph are traversed after lot of monte carlo sampling.

GRAFIT course materials (in .zip) are available from Moodle GRAFIT website - https://moodle.org/pluginfile.php/4765687/user/private/Grafit-master.zip?forcedownload=1