Відео

really great overview of topics. subd n liked each im watching!

💙

can we connect somewhere to have a chat?

Thanks for your awesome video! I just have one question regarding the contents of Memory Bank. As I understand: "Memory from each frame is inserted into the Memory Bank", but then you mentionned that the memory bank contains 3 elements, so is it in reality (memory + prompts + object pointers)= memory bank ? Thanks for your answer!

Btw, I'm not a football fan, and although you mentioned in the note that it is okay .... no, it's really not ... even when I watched this video a few times, who did what still doesn't register in my brain...

I just tried it. However it failed on the first try of the "basic" stuff. With GPT4, the BasicQA keep returning "Question: .. Answer: ..." , but I only need the answer itself, not the whole "Quesion: ... Answer: ..." .... So, which part I don't have to worry about the prompting part?

if the 60m parameter model does 50% accuracy, how can you improve this without using a bigger model? Because if you use a bigger model, then it actually just memorizes the data better. So it is basically overfitting, isn't it?

Can I connect multiple lora adapters at the same time to the base model?😊

Great explanation ❤

Great video! In my opinion, the explanation of loss is missing, but overall it's a very easy-to-understand video.

Some more information at 10:25 - In the token to image attention, the query comes from the prompt + output tokens and the key, value comes from the image. In the image to token attention, the query comes from the image embedding and the key, value comes from the prompt + output tokens.

At 10:03, 4 new tokens are added to the sparse embeddings, 1 representing the IoU score, the rest of the 3 representing the masks. Just a minor correction.

thanks for the video! It will be great to see how to use DSPy for agents.

Amazing work! Thank you!

Great work

Thanks for sharing this amazing tutorial

Great job! I have used your videos to help several recent grads, who had some gaps in understanding. You are a very good teacher.

Awesome content, u can please try some text-grad? The Stanford guys release a paper about this in July

When this module came out the docs were very confusing. thank you for such a great explanation

was hoping to make the prompts autonomized. i feel like you still need to understand prompting well before you can use this :(

Thanks!

i have a question can i use any other models other than openai? im running my own models in deepinfra.

As a German I enjoyed the chosen example a lot 😄

Great video man, also loved the one piece T-shirt ;)

Thank you:)

Plz share the colab in this video, not dspy example.

Also at 12:42 I am getting: answer='Mario Götze' confidence=0.9 not Andre Schurrle I quadruple-checked that my code is the same as yours.

This is the only video or resource I've seen on DSPy that makes ANY sense. Great job!

Greate explanation. can i have link for the Notebook that you have show on the video

I kinda feel guilty that I am seeing such content without paying anything! This is gold...Thank you!

Great video. However , dspy seems to be very fragile; it breaks easily. e.g. at 11:00 you ask ""What is the capital of the birth state of the person who provided the assist for Mario Gotze's in the football World Cup finals in 2014?"" and it answers 'Mainz', which you said is correct. But if I make the question slightly different by adding "goal" after "Gotze's", so the question is now ""What is the capital of the birth state of the person who provided the assist for Mario Gotze's goal in the football World Cup finals in 2014?"", it answers "Research".

You do realize that GPT 3.5 Turbo was deprecated aka it no longer exists.

Yes boss! Subscribed! Great video and very much untapped territory, the only well made tutorial for dspy!

Thanks man!!!

Thanks to you, my friend, I learned what I haven't understood for days. I insistently want to learn dspy, but I didn't understand it. Thanks a lot.

Excellent 👌

Excellent video. Thank you. Can I grab the resulting prompt? I know it is supposedly a new paradigm which abstracts it away, but some may still want to revert back to using a simple prompt in prod. post optimization

Thanks Neural Breakdown with AVB! I always wanted to know how "latent space arithmetic" works.

Awesome video. Thanks mate🤩

Thank you! As the others have commented, a great explanation, as always! At 4:44, should the purple bars be flipped about the "x-axis" from what is shown, since at the left edges of the contiguous red "stretches"/"blocks", the only part of the kernel that overlaps is the negative/"down" bar, and vice-versa at the right edges? Thanks again!

thank you sir, i subscribed your channel after watching this video

This was a useful summary for finding papers to research developments in multimodal machine learning models!

Gold and underrated channel, hope you get the recognition for your good work.

Great video.

Great video as always! This is the best ML channel on UA-cam!

Thanks a lot for these insights on Segment Anything 2 (SAM2)

Awesome video as always, keep producing epic content and the masses will come! :)

Great video!