And since B is the max element, this justifies the interpretation of the log-sum-exp as a 'smooth max operator'

Really well explained, thanks.

Great video.

Drake Forges

I'm confused about the deriative of a vector function at 5:40, i think the gradient of a function f:Rn→Rm should be a matrix of size m×n. not sure about it

Brown Paul Wilson Deborah Harris Shirley

2:59 actually the pseudo algo you are using is 0 index.

Wonderful Lecture. Thank you

Cuz world wars!

Allen Shirley Miller Elizabeth Thomas Linda

For AnyOne having any doubts in relation bt NLL abd Cross entropy . this is a must watch !!!

This helped a lot. Fantastic intuitive explanation.

Hall Margaret Jones Angela Wilson Larry

Great video series! The algorithm video was the one that finally got me to "get" DTW!

Lopez Sharon Davis George Taylor Laura

Your content is amazing !!!

So if I have a list of categorical inputs, where the order indeed imply their closeness, then I should not use one-hot encoding, but just use numerical values to represent the categories, is that right?

love this series! you explained the concepts really well and dive into details!

You look like Benedict Cumberbatch

Thanks for posting Herman, super insightful!

You are good

Awesome, very great video!!

I am not a student at your university, but I am glad that you are such a good prof.

I learned a lot as an Azerbaijani student. Thanks a lot <3

Really great explanations. I also really like your calm way of explaining things. I get the feeling that you distill everything important before recording the video. Keep up the great work!

Thank you

bro just keep teaching, that is great!

These videos are sorely underrated. Your explanations are concise and clear, thank you for making this topic so easy to understand and implement. Cheers from Pittsburgh.

Working in NLP myself, I very much enjoy your videos as a refresher of the current ongoings. Continuing from your epilogue, will you cover the DPO process in detail?

your way of explanation is very good

Thomas 🤣

good sir

thank you very much professor.

One of the best explanations on PCA relationship with SVD!

Why is it prefered to solve the problem as minimize the cross entropy over minimize de NLL? Are there more efficient properties doing that?

Thank you, really great explanation, I think I can understand it now.

Thanks for lecture.

With regards to the clock analogy (0:48): "If you know where you are on the clock then you will know where you are in the input". Why not just a single clock with very small frequency? A very small frequency will guarantee that even for large sentences there will be no "overlap" at the same position in the clock for different positions in the input.

The best explanation!

Great explanation!! Thank you so much for uploading!

Great video. That meow from the cat though

Thanks ! great video

This is one of the better explanations of how the heck we go from maximum likelihood to using NLL loss to log of softmax. Thanks!

Great Explanation

Thank you

Sticking to a simple Git workflow is beneficial, particularly using feature branches. However, adopting a 'Gitflow' working model should be avoided as it can become a cargo cult practice within an organization or team. As you mentioned, the author of this model has reconsidered its effectiveness. Gitflow can be cognitively taxing, promote silos, and delay merge conflicts until the end of sprint work cycles. Instead, using a trunk-based development approach is preferable. While this method requires more frequent pulls and daily merging, it ensures that everyone stays up-to-date with the main branch.

It very clear explanation, thank you very much!

Does this algorithm work with negative instance? I mean can i use vectors with both negative and postive values?

Good explanation. Thank you Herman

Hello Herman, first of all a very informative video! I have a question: How are the weight matrices defined? Are the matrices simply randomized in each layer? Do you have any literature on this? Thank you very much!