Відео

This is an amazing resource. Many thanks for putting it together and providing us for learning 🙏🙏

Thank you for all the knowledge and materials

毕业设计太tm烦了

Amazing lectures so far. Thank you. What software are you using for the presentation? Very clean and organized.

Thanks! Great Lecture!

these are amazing

Thank you for preparing these video courses. Can I access the jupyter notebooks used in the videos?

im glad I ran into these lectures! Great job!

thx for providing this high quality lecture ! Great resource

Thank you, but I'm curious instead of that can we use information from a table of a csv or xlx file ?

Thank you

Thank you for lecture

Thank you

Great videos, thanks so much for posting them!

I have question, my ontology class called "Student", and (14053 ) individuals, and I create (136 ) data property, "prediction" as integer(can be "1" or "0"). The Reasoner (mine is Hermit). 14053 divided into 4 courses 1- 2460 A 2461- 6482 B 6483- 8694 C 8695-14053 D I want to predict a student’s final performance in course A, and the ontology automatically predicts the other courses B, C, and D?

Nicely explained 👍

may be it is me being stupid but ca not find link to the example being used.

where can i get that ontology example you are demonstrating?

Shape is like class definition in oop or database schema

I wish it was something ```?year wdt:yearOfDate ?date``` instead of additional functions. Request for future revisions :)

Hi Dear, I like your videos very much. As a UA-cam expert, I analyzed that your UA-cam video is not getting proper views and subscribers. The reason is that, video isn't SEO completed, no low competitive and ranked tag used & didn't optimize the channel with white-hate SEO. If these issues are resolved, then your UA-cam video views will increase. Do you know UA-cam algorithm which channel prefer and increase their video impression & search result first?

10:50 I am confused by the example: st2:GeneRoddenberry st2:birthPlace st2:ElPaso Why st2? Maybe st1 instead?

Vielen Dank für die gute Einführung.

chatGPT can output not just text? Interesting :O

😎 merci

This is an excellent series of lectures, thanks for publishing!

Really good overview!

excellent video!

Very nice nice presentation........❤

This is great,thank you so much

This is the future . Thanks professor🙏

Thank you Professor for your very clear lecture. I have a question: What tools did you use to prepare your lecture slides and figures/ diagrams as they look super nice?

great video, so clear, thank you very much !

Thank you!

Danke für die verständliche Erklärung! Habe durch Sie endlich das Thema verstanden.

Is anybody can recommend some manuals or papers about implementing programming language or system atop the graph of objects the way Lisp uses lists for its internals? I mean the use of interlinked (Python) objects which represent both software and data it processes in a homoiconic way.

Thx from me, too. Just a question: where would Neo4j be positioned in this programming model?

thanks for this lecture

thanks for this lecture

thanks for this lecture

Love the reason why it's called JSON-LD instead of JSON-RDF (at the end of video) :-)

thanks for this video

Very informative lectures for beginners. Must listen

Very helpful lecture! Thanks a lot for this awesome course.

horse story at 09:14

Pure gold. Thanks a lot!

Sorry, on slide 7 (2:50 - 3:35), of course I'm referring to "knowledge" and not to "logic". Probably this was some Freudian slip ;-)

The presentation is really good. Very interesting to listen to. Do Derivation and Inflection work the same way for the german language too ?

Interesting trailer. 👍 Subtitles seem to be automatically generated and are a bit confusing or there‘s a new denglish version... 😉

Thank you for the interesting lecture. I added a link to this video in the Wikipedia article "Finite-state transducer" w.wiki/Wp5 If you change the license of this video from Standart UA-cam license (closed license) to the Creative Commons Attribution (open) license, then I can upload this video to Wikimedia Commons and Wikipedia.