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
Unified course materials collated from design and commit notes of earlier NeuronRain FOSS repositories - as single .txt file (frequently updated) - https://github.com/shrinivaasanka/Krishna_iResearch_DoxygenDocs/blob/master/text/NeuronRain_Documents/NeuronRainDesign_unified.txt (18466 pages on 16/8/2026 - 50 lines per page) - supersedes erstwhile GitHub Grafit Virtual Classrooms: https://classroom.github.com/classrooms/8086998-https-github-com-shrinivaasanka-grafit
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. () Conventional classroom teaching based on textbooks has been more examination-oriented than learning-oriented (Example: 2 kinds of academic examinations - knowledge tests in Medicine focusing on recall from a textbook corpus versus IQ tests focusing on analytical ability in STEM that rarely search a knowledge corpus - distinction in section 802 - https://shrinivaasanka.github.io/Krishna_iResearch_DoxygenDocs/) - Knowledge graph of textbooks is a visual teaching aid for both memorization of knowledge and IQ.
() Understandability - No one learns a concept in first attempt - e.g Understanding a theorem-proof often requires repetitive study () Rote learning is often necessary for knowledge tests that seek to remember facts (e.g in medicine Purkinje fibre can't be derived mathematically but is an axiomatic fact that has to be memorized), but may not help in reasoning and IQ tests (*) Learning how to learn is subjective and differs from person to person.
(*) Classroom teaching has been conventionally and traditionally linear and dependent on textbooks covered chapter-by-chapter. Academic textbooks could be converted and condensed to Knowledge Graphs by (1) Recursive Gloss Overlap-Recursive Lambda Function Growth PySpark-Python algorithm implementation in asfer-github-code/python-src/InterviewAlgorithm/InterviewAlgorithmWithIntrinisicMerit_Crawl_Visual_Spark.py that creates a TextGraph from text document based on WordNet-ConceptNet or (2) asfer-github-code/python-src/KnowledgeGraph.py that extracts relation triplets by Spacy-REBEL-PrimeKG which are eventually edges of KnowledgeGraph version of text.
(*) An example class 12 Computer Science textbook excerpt downloaded to course_material/TextBookKnowledgeGraphs/TNTextBooks_Class12_ComputerScience.txt from https://www.tntextbooks.in/p/12th-books.html - Chapter 4 - Point 1.1 - that defines an algorithm has been converted to a Knowledge TextGraph through asfer-github-code/python-src/InterviewAlgorithm/InterviewAlgorithmWithIntrinisicMerit_Crawl_Visual_Spark.py at https://github.com/shrinivaasanka/asfer-github-code/commit/4a92c7c03fa09707afe807b763234588ca329505 with a correspoding graph plot at course_material/TextBookKnowledgeGraphs/TNTextBooks_Class12_ComputerScience.jpeg
(*) Visually rendered TextGraphs-KnowledgeGraphs from textbooks assist students in relating concepts across pages of a linear textbook and offers a holistic bird's eye view of the subject (especially k-cores of a textgraph-knowledgegraph highlight prominent concepts in a text that are essential in learning process). Earlier textbook example used a linguistic ontology (WordNet) to learn knowledge graph on computer science which caused certain deviations (e.g "tree" datastructure is inferred as "botanical tree" due to incorrect context) in word sense disambiguation. Ideally every subject textbook parsed by an ontology specific to the field could parse accurate knowledge graphs - Following are some of the major field specific knowledge graphs,books,papers and tools published recently:
- Medicine (unifies Biology and Chemistry as Biotech) - PrimeKG - https://zitniklab.hms.harvard.edu/projects/PrimeKG/ (a presentation on KGs in medicince - BiorXiv,CRISPR etc., - https://www.plasma-mds.org/files/fdps2022_d3-e2_lorenz.pdf)
- Physics - https://ieeexplore.ieee.org/document/9776230/ - Python tools for Physics KG
- Mathematics - Math-KG (paper: https://arxiv.org/pdf/2205.03772) - https://github.com/wjn1996/Mathematical-Knowledge-Entity-Recognition
- ConnectedPapers and SciSpace - KnowledgeGraphs for academic publications - https://scispace.com/resources/connected-papers-or-scispace/ - Both ConnectedPapers and SciSpace index millions of publications and elicit relationships among them
- Knowledge Graphs for Textbooks: Extraction and Completion Techniques - https://ieeexplore.ieee.org/document/10381546 - Knowledge Graph for Conceptual relationship in academic textbooks
- PREAP - Annotation Protocol for Textbook Enrichment with Prerequisite Knowledge Graph - https://link.springer.com/article/10.1007/s10758-023-09682-6
- Knowledge Graphs - Fundamentals, Techniques, and Applications - By Mayank Kejriwal, Craig A. Knoblock and Pedro Szekely - https://mitpress.mit.edu/9780262045094/knowledge-graphs/
- Textbooks to Knowledge Graph by Large Language Models - Neo4j LLM graph builder - https://neo4j.com/labs/genai-ecosystem/llm-graph-builder/
(*) Existing KG algorithms extract relation triplets from flat text through deeplearning neural networks. On the contrary NeuronRain textgraph-knowledge graph extraction adopts a formal approach based mainly on following theoretical frameworks and inference of lambda expressions from randomwalks over textgraphs of sentences which could be termed deeper-learning:
- Frege Principle of Compositionality (Recursive Lambda Function Composition) - https://www.nltk.org/book/ch10.html and https://philosophy.ucsc.edu/courses/images/Frege_%20ON%20SENSE%20AND%20REFERENCE.pdf (Most cited article by Frege on how to analyze sense and reference in a sentence)
- Phrase Structure Grammar - Noun Phrase(NP) and Verb Phrase(VP) structure and Treebanks - Section 6 - https://www.nltk.org/book/ch08.html#ex-phrase-structure-tree
- Dependency Grammar - https://www.nltk.org/book/ch08.html - Section 5 - Head and Dependents in a sentence
(*) Knowledge Graph based Question-Answering - from various examinations - AI-RAG KGGen Visualization of Academic Textbook Concepts - HTML and Image graphics - https://github.com/shrinivaasanka/Grafit/tree/master/course_material/TextBookKnowledgeGraphs/testlogs
(*) GRAFIT course materials (in .zip) are available from Moodle GRAFIT website - https://moodle.org/pluginfile.php/4765687/user/private/Grafit-master.zip?forcedownload=1 (Defunct URL)