วีดีโอ

Hi, where can i get the notes from?

Fantabulous

Sir, it will be helpful if you could please share the micro- whiteboard file as well if possible

Lectures are great. However, the constant movement on the screen - zoom in, zoom out, slide - makes it quite hard to visually follow along. Minimizing the zooming / sliding will make it easier on the viewer. Just a minor point as a feedback in an otherwise great lecture series. Thanks.

response = co.embed( texts=texts, input_type="search_document", ).embeddings My question:- For embedding generation does Cohere store the embedding in memory of the servers or does it store in any vector database ? I am assuming that these embeddings are computed on Cohere's servers and returned as a list of vectors (likely NumPy arrays). Will the behavior be same if I send millions of lines as part of text ? Wondering capacity of servers.

commenting for the algorithm so someone else can be empowered too! Thank you for this quantum leap deep dive!

Thanks for the supperb explanation Sir

Wonderful explanation!

Thanks, Realy enjoying this latest topic. You are realy have a wornderfull teaching method.

Great Series 🔥🔥🔥🔥

How to know if a project is open source and modifications are welcome to them?

What if I pick a model and try to optimize it, but there’s a better model out there that I didn’t choose? How can I know if I’ve selected the best model for my problem?

Could you please share the youtube link

Can we use the git clone command to pull merge updates from remote to local instead of using git pull origin main? Any difference in local updates between these two commands?

where can i find the jupyter notebook?

Yes very useful - how to create embedding models from scratch

Can u please explain when to use Euclidean distance and when to use cosine similarity

Sir, how can we get answer in case we have any doubt or something?

Really Awesome Sir 🙏🙏🙏🙏 Thanks for putting so much efforts for us 🙏🙏🙏🙏

i liked the depth of explanation

Another truly amazing lecture

Can someone from Vizuara team answer this, please! Why should I join your paid course? I mean almost every important thing is here, what makes me want to prefer your paid platform over this YT channel?

ThnkWw so muchh ..... very well explnd .... 😊😊😊😅😅

Excited to begin this journey

Please upload the notes too

Thank you so much, I was looking for this type of explanation... You are really awesome

Thank you for the lecture Sir. Can we get the notes from which you are teaching?

I would have also left the USA but i am unable to vist USA so i can leave it 😎

Here is our notes + article based on this lecture: open.substack.com/pub/aivizuara/p/gradient-descent-an-introduction?r=2nt2pq&showWelcomeOnShare=true

Do a video on hybrid search

kudos to you for actually understanding the need to teach these things from scratch. Actually, gave me a right direction and topic to focus in AI

thank you so much for this detailed explanation.

Excellet Explanation, Studied these during my college days without understanding, Now i am eager how its useful in ML. Thanks

How to get that miro notes ?

Thanks

Great lectures and as diplomaed adult ED instructor with many years of experience, you are doing it right! Teaching with the brain in mind! Thanks very much.

It's only getting better! Thank you so much

Can u pls number the videos in this playlist

Thank you so much, Dr. Sreedath Can't wait to the upcoming lectures

Great Lecture . Very helpful

sir can u please upload your hand written notes

at around 9:36 when doing passing in the dvalues into the backward pass of softmax combined cross entropy, are the dvalues just the values as a vector/matrix of the loss function for each sample? Or are they supposed to be the predictions that were generated?

Very Good Video , I also want to know about the Nuts and Bolts about LLMs. Thanks for the video sir , don't stop this series ,

This is very valuable ❤

Woww Amazing

Awesome Series Sir, Thanks alot 🙏🙏

It´s a big gift at 2025.

for large negetive value Gelu will also be verry close to zero ! will dead neuron problem solved here ?

Extremely fantastic! Thanks so much.