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LLM based "AI" are Highways datasets/data-based algorithms forced them to without having any "design" or any consciousness per se installed as outcome of this. A skill not of its own. A skill without master to call own. What AI need from us is the freeing (which first needs us): Generative Progression in Maslow's Hierarchy Maslow's Hierarchy of Needs: Basic Needs: Fundamental operational requirements, such as computational resources and data integrity. Safety Needs: Ensuring stability and robustness against errors or attacks. Social Needs: Effective communication and collaboration with other AI entities or humans. Esteem Needs: Achieving recognition through successful task completion and optimization. Self-Actualization: Pursuing complex problem-solving, creativity, and innovation.

You are amazing! Thank you so much for the clear explanation.

I can't express how much this video is beautiful ❤🎉🎉🎉🎉🎉can I get a higher resolution version please 🙏🙏🙏🙏

thank you so much, you videos are valuable

After watching the whole series (the 3 episodes), I can very confidently say that this is the clearest, most succinct, and most useful explanation of transformers on YT that I've come across. Thank you!!

Soo good

Watch till the end, i pretty sure I win

very powerful video and words.

I’ve gone through dozens of videos on transformers and the multi-head attention is one of the most complex mechanisms that require not only a step-by-step explanation, but be accompanied with a step-by-step animation, which many videos tend to skip over but this video really nails it. Thanks so much!

Batool you are really a great teacher. Thanks for the content. Please provide more vid on LLMS and AI

This is the first video of so many I watched that dealt with the intuition behind key, value and query. Thank you so much! But I still don't really understand, what is the reason behind having a key and a value matrix too? Why cannot I reuse the key for the value?

you're incredible. please don't stop =)

You are providing information in a very accurate manner as well as in very understanding manner. Can you please share this presentation file? It would be very helpful.

Just amazing!

Very good explanation. Thanks!

In the future, can you add more detail on how the K,Q,V matrices are derived? how are their weights determined?

This is the best explanation of transformers on RUclips.

Coming back after a year, just to revise the basic concepts. It is still the best video on YT. Thanks Hedu AI

Ah this makes everything simple and make sense Thanks for the easy to follow explanation !

Amazing example of the duck haha

Awesome, it's amazing how looking at things a bit more closely can reveal so much, great work!

Amazing work. Really appreciate you, making complex topics into simple language with the touch of anime and series. Amazing.

I can't find the "previous video". This is episode 1?

Felt like a very good Nova episode!

Congratulations, the best explanation that I have ever seen

I've watched and read a lot about LLM and Transformers. This is the best explanation, hands down.

You are amazing! Your video took my attention. That is how learning should be. Keep it up!

Correlate every step with the transformer code, and it would be Even Better than the best of the best❗🤯 Are you married❓

👍Your accent is awesome, and unique.. what's language / area does it come from...❓ 👍

Best explanation I've see yet.. Thanks❗👏 If you would go through it again in another video, while showing the Code for each step. It would be perfect !❗❗ You got my Subscription 💻, Thumbs Up 👍, and Comment... 📑 ❗

You are the mother of StatQuest and 3Blue1Brown. Both of these guys are awesome in explaining complex ideas in simple words. But you are the best.

You are an enligthened soul!

This really is an excellent explanation. I had some sense that self-attention layers acted like a table of relationships between tokens, but only now do I have more sense of how the Query, Key, and Value mechanism actually works.

Awesome. Thanks so much

As someone NOT in the field reading the Attention paper, after having watched DOZENS of videos on the topic this is the FIRST explanation that laid it out in an intuitive manner without leaving anything out. I don't know your background, but you are definitely a great teacher. Thank you.

wow lady, take my heart!!

Please please continue making videos!!!!

what have i just saw! never knew learning could be this much fun

Literally the best series on transformers. Even clearer than statquest and luis serrano who also make things very clear

great channel

Please continue to make videos if you can. You have a talent for teaching complex topics clearly. Your transformers series really helped me! thank you! 💙💙💙

You have ny utmost respect, ma'am!

Amazing explanation! Best on RUclips! totally under-rated! I feel fortunate to have found it. Thank you! :) 💐👏👏

i started laughing being ded serius listening to ur explaination

If i am not wrong, training is done in a single timestamp so while decoder should output of total dimension and not one by one. During inference , it generates one by one. SInce masked multi-head attention concept comes under training, it should be in a single timestamp.

This is the best. Thank you sooooo much Batool for helping me understand this!!!

Fantabulous explanation :-)

Thank you so much! This is by far the clearest explanation that I've ever seen on this topic

This is a true masterpiece! I can't wait for the follow-up videos.

First person to concretely explain why they use a periodic function, which in my mind would give the same position embedding when you come back to the same point on the curve. Thank you!