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Thanks for sharing this video, it's really helpful. Could you please share the full code that is shown in the video and I need some help regarding Llama 3 and I have some tasks to perform using this model so how can I reach you?

Dear sir, I am Computer students from India,i want to learn LLM ,RAG,FINE-TUNE datasets with personal data, please use open source LLMS , share youtube, help me to develop " CHATBOT WITH my Personal Data", so when i can search,it has to reply back...please sir......please sir help me to achieve it regards regards, Students

Notebook link: colab.research.google.com/drive/1YVhu1DAOn_goF_w0t5G8xKZw-hx7BH8V?usp=sharing

Nice 👍

Thanks for sharing 🙏

Link to notebook: drive.google.com/file/d/1evx24o1tN33HAb5eI-hFsQtez1VivdDo/view?usp=sharing

Thanks for sharing this complete notebook with the description and working finetuing scripts. This is really quality content, and deserves a lot of views.

Notebook link: drive.google.com/file/d/1UdPYXP26BEpaE4gxJMMKzIpPj3mSIBPp

Very nice video. Can you paste the notebook.

Nice code but each parameter explanation is missing

Sir , can you make more videos like this , like applying LoRA Q LoRA etc , that would be very beneficial

Superb ⚡⚡

Excellent job with the video.

Very nice video! Love this entire series.

Thanks for this great effort. It helped me a lot to understand how the things work internally, and how to think about the different tasks from tokenizer's point of view.

nice presentation, and to the point.

Quality and helpful content. Waiting for more videos.

Nice and informative.

Nice video, and thanks for sharing

Notebook: colab.research.google.com/drive/1wd2Xo_Jssynz2lEevmFjHGPLGb91vmfU?usp=sharing

Very nice content. Thanks for going into details of the very important concepts.

Nice content. Thanks for sharing this

Great video.

Link to the Colab notebook: colab.research.google.com/drive/1Fay8lVzW9jnvHFiuuMPc4ROxwODgb52N?usp=sharing

Super fast and accurate than in-built cross validation. This video deserves more views!!!

Nice work ! You just missed the following lines in your blog, so it did not work for me until I read your blog : results = tuner.minimize() print('best parameters:', results['best_params']) print('best loss:', results['best_objective'])

Amazing man, how do you suggest using cross validation with this model?

Thanks for this simple Bayesian optimization code

Thanks for sharing the code for this!

The improvements are promising in the video. Thanks for your video.

Good job 👏

Thanks for the deeper understanding

Very nice video. This is a simple illustration of the fraud detector

Nice example show case.

Very good explanation

Nice work 👍

Nice video. Sharing with the friends

Following up on the channel. Awesome content 😄

Following the series to learn for novice starters

Thanks for videos on this crucial subjects. Hard to find quality content.

Research in labs are good options. I am interested in approaching professors to propose a new project.

Thanks for sharing the insights. Private schools are probably not a good choice if equally ranked public schools has accepted me.

Nice work. The samples are good for the starters

The public university tips are very nice to follow for new applicants.

This is very helpful. The teaching assistants are also paid!!.

Waiting for the next video. The python concepts are to the point and easy to focus only on the machine Learning data structures.

Code notebooks are really helpful. This series has helped me with basic concepts.

The explanations are very simple to follow and connect with the code provided. Really nice effort. 🎉

Thanks for the deeper understanding and showing code flow used in common development

Nice video. How to select an advisor when going for PhD. Do you have any advise for the same.