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I have learned LLM from everywhere, first time got the proper answer, what's the exactly mean of LLM. That's incredible

Indeed your lecture series on ML is very informative, Could you please guide us , from where we should solve problems & projects to masters these concept?

great video, learn a lot about vector database

can you please provide me the slide ?

please when i do the command +shift+p it does not show python interpreter what should i do

It is amazing that you have kept a comprehensive syllabus for ML. It would be great if a playlist on Unsupervised Learning and RL is made.

Really enjoying your videos, great work 👍

Thank you sir for taking for your time and sharing these all and that to free❤

This is crazy! Thanks for the idea!

Woah this is so nice, I really appreciate the effort.

what if we are CS Majors and don't have any domain specific knowledge..can you please mention email as it was not clear in the video

Very well explained, 99% concepts got cleared

Thank You For This Amazing Lecture Sir :D

Amazing lecture

Spectacular - regarding vector embedding you said we can train a neural Nework to create token embedding… is this process running on cpu or GPU and how are computational needs for this process vs. trading of LLM

Really good sir

amazing lecture, everything u taught in this was very clear !

Thank you, amazing lecture 👍

Amzaing lecture.....The lecture just finished so smoothly......studied this topic 3-4 months ago but clearly understood it today...... Thank You sir for providing these amazing lectures for free

nice presenting skills. had not touched Python in a while. This was a nice 'set' refresher.

this is gold. and you are right. No one really covers this stuff in detail and in very intuitive way. I still need to replay but it sinks in because you cover the key details.

Just came across this wonderful playlist ,I have been started following your playlist for 2 days and i must say you are true genius I already have good understanding of ml but going through your videos make my understanding more clear and making it simple it also motivate me to think out of box. Thank you sir

I think the minute 36 minute mark addressing "stride" the different color ink made it a bit confusing but I think I got it. I take it that the context size should never be less than the slide amount? Thx.

What is the email, that we can brainstorm ??

TANKQ SIR FOR GIVEN YOUR TIME TO US AND PLEASE SHARE WHITE BOARD SIR WE CAN IT CAN BE HELPFUL FOR BETTER UNDERSTANDING AFTER PRATICISE

These are recorded, so the pace is fine with me. However, I will make sure I try to keep up so I don't feel overwhelmed. I will need to watch this lecture again. The nuances of byte pair encoding based on frequency didn't fall in place for me. Understood in concept but I want to go beyond understanding. I got the book, but will the lecture notes be made available?

It's my 1st time hearing of a Universal differential equation

This series of lectures is highly underrated. It looks under the hood in an easy to understand manner unlike so many other courses out there.

Can you please share document for the links

Why do we need to check the optimal value of k? We will get a good result if we always use a high value of K.

22:55 Sir, when I go to the “Terminal” is telling me that “Python was not found” how can I deal with this? 😢

Thank you for creating this fantastic playlist!!

Another exceptional class … Just fabulous

Sir if you share slides and all other resources that are used it's very helpful. We appreciate your efforts thanks sir.

Why is there a trend to large context length … What are the trade offs large context windows vs small context length … how is context lenght relation to model size

Thank you, sir, for this amazing lecture :D, However, I'm a bit confused at 37:44. I noticed that in the loop, the input chunks and the target chunks are changing with each iteration. So, in the end, will we only have the last part? So what's the whole point??

Really looking forward to this!

Thank you so much. I can't express my happiness within this comment, but my happiness is beyond infinity. I was always wondering about gaining the in-depth concept of neural network and implementation on the project, in which you guys had covered all of its minor details to the perfectualization. Hats off to Dr. Raj Dandekar and to the Team. None of the ML courses had covered this much detail . You guys deserve a huge respect. I would like to request if you could make the tutorial over the data visualization before creating the ML model such as heatmap, scatterplot, pairplot and others.

I've been following this tutorial very closely, absolutely amazing!

When the value of K is high, the overall time complexity will be increased. How to decrease this complexity. Is there any way to decrease time complexity as well as get the best output?

Good Class

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One interesting fact: "The quick brown fox jumps over the lazy dog" contains all 26 letters of English Alphabet

Very nice

God Tier Explanation. This is exactly what I have been searching for since the past month

I am happy for you, Dr. Raj. This is really inspirational, thanks a lot.

Hi, from where can I access the lecture notes? It would be great if you could share the lecture notes so that we can go through them after the class.

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why we are not calculating information gain and directly selecting root based on entropy?

why don't you consider age as a root?