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Thanks for the suggestion! A video on fine-tuning LLMs is definitely coming soon...

Thanks

Sure, I will try to provide it asap...

@@airesearcher24 Thanks

Glad to know that 👍

@@airesearcher24 Thanks for the great video.👏 But, the same thing i have done myself. when i am asking the very simple question the response from the gpt2 is so weird..... why it so?

Hi, you are right 👍 GPT-2 is an older LLM which can cause such issues. You can consider replacing GPT-2 with Llama2/Llama3/any updated LLM instead...

I think, the five outputs (phi_{0,1,1}, phi_{0,2,1}, phi_{0,3,1}, phi_{0,4,1}, phi_{0,5,1}) for the input x_{0,1} are different because each neuron has its own distinct set of learnable/trainable coefficients... These coefficients adjust the B-spline activation functions in each neuron which allows each to respond differently to the same input based on what it learns during training...

I used the 'KAN Conv MLP2' architecture as outlined in the referenced paper. The authors developed this architecture through trial and error process. However, you have the flexibility to tweak this architecture's hyper-parameters to better fit your specific needs. For more detailed information, you can review the paper (arxiv.org/pdf/2406.131559).

Glad it was helpful:)

It's great to hear you are working on KAN-MLP research. In the video, I used MNIST dataset. Here is some code that might be useful: github.com/manishasirsat/Convolutional-Kolmogorov-Arnold-Network/blob/main/convolutional-KAN.ipynb

@@airesearcher24 Thank you

Glad you enjoyed it

Can you suggest some reserch papers which relate to the topic belongs how human brain process images

Can you suggest some papers related to topic " ai and human brain "

Might be an interesting paper on brain image processing: www.sciencedirect.com/science/article/pii/S1053811917308613

Amazing its like encyclopedia it helps me a lot thank you for your valuable time

Thanks :)

Yes, you are right

Glad it was helpful!

Here are couple of interesting papers: 1) link.springer.com/content/pdf/10.1007/s42979-021-00815-1.pdf; 2) hal.science/hal-04206682/document

Great, you are interested in trying this. Here are some smaller models that you could try: DistilGPT-2; BERT Small; RoBERTa Base; T5 Small; BERT Mini; GPT-2 Small

thanks:)

Thanks:)

I am going to try that soon. Thanks for the input :)

Great!

Yes, that's correct. You need to pay for credits to use the OpenAI's models. For free access to some LLMs, you can refer to this video: th-cam.com/video/c0PXr1Eazr8/w-d-xo.htmlsi=qrpA4r7sAHbAeZjE

Thank you for your great Video! Unfortunately the commands doesn't solve the import error for me :( Do you have another idea?

Ok, I am going to check it again..

Glad it helped.. 👍

Thanks:)

Thanks you:)

Yes, you are right, Thanks for noticing it 👍

Hi, the motto of this channel is to connect theoretical foundations with practical applications in AI, covering machine learning, deep learning, large language models, computer vision, and more.. To make the best use of the videos, take detailed notes, practice coding exercises, and work on small projects to apply your knowledge... Stay consistent for the best learning experience:)

Thank you:)

Thank you! Glad you found the video helpful.. Your feedback means a lot to me:)

yeah me too i have the issue as well

Let me check the error and I will get back to you..

@@airesearcher24 Thanks for the reply. The exact error that is showing is as follows - ImportError: cannot import name 'indices_to_mask' from 'sklearn.utils._mask' (/usr/local/lib/python3.10/dist-packages/sklearn/utils/_mask.py)

@@airesearcher24 Hi, I hope you're doing well. I'm currently stuck on a portion of the tutorial due to the error and was wondering if the issue has been resolved. Any guidance would be greatly appreciated!

Guys, please try these commands. Hope this will solve the error:) 1. %pip uninstall imodelsx 2. %pip install imodelsx==1.0.5 --force-reinstall

Hi, I do check. If I missed yours, I will take another look..

Thanks!

Sure, I will make a video on that. Thanks for the suggestion. Keep watching:)

Glad you enjoyed it! Stay tuned for forthcoming videos:)

Thank you:) keep watching 👍

Hi Lim, thanks for reaching out. Your interest is appreciated.

Great, enjoy catching up on the content and feel free to reach out if you have any questions/comments.

Glad that you are enjoying the content. Welcome to the community:)

Hi, I think to address catastrophic forgetting in a bidirectional LSTM with layer normalization while ensuring SGD convergence and maintaining weight matrix orthogonality, implement Elastic Weight Consolidation for stability, use orthogonal regularization, adjust learning rates, and apply gradient clipping. Hope this paper might be helpful: Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A. A., ... & Hadsell, R. (2017). Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences, 114(13), 3521-3526.

@@airesearcher24 Thank you

Glad it helped:) keep watching 👍