Name the book from which you took this determinant and linear independence relation

Very helpful ma mannnn <3

Quite appreciate your sharing

Hi! I really like your videos! I wanted to ask, why did you "hide" the rest of the videos on the LangGraph playlist? Is there a platform or some place else I can finish watching them?

Hi! I really like your videos! I wanted to ask, why did you "hide" the rest of the videos on the LangGraph playlist? Is there a platform or some place else I can finish watching them?

Hi! I really like your videos! I wanted to ask, why did you "hide" the rest of the videos on the LangGraph playlist? Is there a platform or some place else I can finish watching them?

Hi! I really like your videos! I wanted to ask, why did you "hide" the rest of the videos on the LangGraph playlist? Is there a platform or some place else I can finish watching them?

Great!

Amazing video

great video mate keep helping and teaching people request - can you just change sequence of videos in your playlist it'll be easy to watch in earliest first form

Do you think working with tools as 'functions' instead of python classes is the best? I saw some people creating classes to do it, not sure if it's the preferable way to orchestrate.

Very well explained! Thanks for the videos

That just made “state” so easy to understand for me

Really like your content man

How this dude only has less than 1,000 views is beyond me

Great Video! Appreciate

can i get a github link or any link where i can find the code?

When implementing the example, you may have an error in the tokenize_input functions. This function assumes dynamic padding, but in new versions of hugging face, this is implemented through collator. padding = "max_length" does not mean dynamic padding, but padding with a fixed value Example (truncation = "True", padding = "max_length", max_length = 120) The error that appears after deleting padding = "max_length" occurs for the following reason. return_tensors = "pt" assumes the return of the tensor. The tensor has the property of equality of all objects inside it in length, and they are not aligned with us. The correct option tokenizer(prompt, truncation = True).input_ids And then use collator. Here is an example: kzbin.info/www/bejne/baKYf6x8iZ2Fe8k kzbin.info/www/bejne/pKeliZltqJypiqs huggingface.co/docs/transformers/main_classes/data_collator Also note that each task has its own collator. Experiment)

Thank you very much!

Hello i'm korean high school student and i'm here for my report about svd. I searched lots of videos but your one is the best one to understand. Thank you

Part 1 makes it pretty easy to guess what part 2 is going to be, so viewers can do it on their own and use this second video as a check. Maybe you didn't plan it that way but it works out nicely.

Saw this proof in a book but the notation was so confusing, came here to the web for a demonstration with plain English description, as you have done. Thank you.

very good

this is cool, many thanks, bro !!!

You are amazing thank you

Many many thanks for this video. It helped me a lot!

Great video! Could you share a link to your colab notebook?

Thanks. This helped a ton!

Sound is too low!!

Thanks

Thanks for the video. How can we create our own dataset for text summarization and how big should it be to train the model properly?

Finally. The best explanation of top_p 👍 Thanks

Promo SM

Dude, this is a great series of videos. I just want to document that you only had ~500 subs at time of writing (03/2024) for when your channel blows up

Thank you, this is really usefu

You should actually declare variables like: int myvar {7}. Not with the assignment operator (=). That's considered best practice and is the preferred method recommended by Bjarne S.

this is good but where is GitHub code ?

Hi, thanks and I am glad about your video. just is it possible to share codes in GitHub or any other platform? It can be so practical. Also , we are eager for more videos regarding ChatGPT 🙂

Hey ,please could you send your code ?

This is great info, but you do not include links to your notebooks anywhere. It would add a ton of value to have them available in order to follow along with your instructions.

Great video, Ty!🎉❤

What if your embeddings contains both simple text and then json to support the text whatever the text is explaining about. How can we tell the model just parse the json part of the retrieved doc and not text and when replying reply both?

Hi, do you have a twitter or something? Can I contact you? Many great videoes!!

Quick and simple, thanks!

Thanks for the video! Where can I find the inner product video you were referring to in the video? I wasn't able to find it.

Good video. Are you available for consultation?

Very helpful ,if possible write clearly please 🙏

Really good video guys!!!! 🤩😘

This was really helpful for abstracting my understanding of vector spaces. (And gremlin spaces.) Of course I was waiting for the null gremlin space in R where the x and y axis cross because I've heard gremlins don't like crosses. Keep up the good work.

𝐩𝓻Ỗ𝓂Ø𝓈Ｍ 💋