When we use OpenAi API key we make the chunks of data before embeddings while using Gimni API key we did not make the chunks /text splitting is it possible due to large context window of Gimni modal?
- Retrival Augmented Generative with chroma db, n limittation LLM, 5:39 -embeddeding data function to memory chroma db perhaps.. 8:20 - coffecien correlation heatmap semantic data, 10:20 -embeddeding vector data is, 19:42 -LLM Decoder to embedded backbone, 37:22 -victories data embeddeding opens source n cost of gugel, manajize, 24:16 -conventional methods like extrating ocr, 41:26
Thanks for fixing all the issues. The quality really improved. Kudos to the team.
It was interesting. It was the middle of the night here in East Africa. I am now watching what I had missed on UA-cam.
i know right
Thank you for the opportunity !😃
Thanks for such a great lecture. I wish I could attend live.
Really liked the quiz at the end of sessions.
Very interesting especially with the reference we are provided
Thank you for yout time and answers, this was interesing!
When we use OpenAi API key we make the chunks of data before embeddings while using Gimni API key we did not make the chunks /text splitting is it possible due to large context window of Gimni modal?
Can you please let me know that how to access Google Collab Notebook
Hi Google team and every one; Hello from Bay Area CA
Thnx for generous knowledge-sharing
Thank you! Kaggle and Google.
thank you. Great course👏👏👏👏
thanks For kaggle and Gemini also for Google
Thanks for this Day 2
Thanks,,,, for shrring the recordings
THANK YOU❤BRASIL🇧🇷👋
Thanks lot, a very good teaser
No way Kaggle notebook is getting save. It is always getting eshausted token limits. Thanks
Try quick save rather than the save and run option
B
Cnn
C
D
Indeed, this is very useful.
Thank you! Appreciate it!
very informative course
thx kaggle 🙏
- Retrival Augmented Generative with chroma db, n limittation LLM, 5:39
-embeddeding data function to memory chroma db perhaps.. 8:20
- coffecien correlation heatmap semantic data, 10:20
-embeddeding vector data is, 19:42
-LLM Decoder to embedded backbone, 37:22
-victories data embeddeding opens source n cost of gugel, manajize, 24:16
-conventional methods like extrating ocr, 41:26
exciting, wonderful
very interesting course
very informative
Informative
Thank you!
GREAT
Atleast make these videos ad free
D
omg, no code, no result of experiment, just talking?
He is talking about the paper and the code shared in a notebook on kaggle.