Відео

Really love the way you broke down the DPO loss, this direct way is more welcome by my brain :). Just one question on the video, I am wondering how important it is to choose the initial transformer carefully. I suspect that if it is very bad at the task, then we will have to change the initial response a lot, but because the loss function prevents from changing too much in one iteration, we will need to perform a lot tiny changes toward the good answer, making the training extremely long. Am I right ?

Very nice lecture on attention.

Now whenever I watch Serrano's video, I first like it and the start watching it coz I know the video will gonna be outstanding as always.

Liked this video and subscribed your channel today.

Amazing video... Thanks sir for this pictorial representation and explaining this complex topic with such an easy way.

Thanks for the simplified explanation. Awesome as always. The book link in the description is not working.

This was very helpful

The best explanation & depiction of SVD.

Thanks for the excellent explanation! I used to know the KL Divergence, but now I understand it!

Great one, the simpler it looks and harder to build!

This and your whole series of attention NN is a thing of beauty! There are many ways of simplifying this here, but you come the closest to understanding Attention NN and QC are identical and QC is much better. In my opinion QC has never been done correctly, the gates are too confusing and poorly understood. QC is not still in simplified infant stage, it is mature what QC can do and matches all Psychology observations. All problems in Biology and NLP are sequences of strings.

awesome!

Thanks!

I would like to say thank you for the wonderful video. I want to learn reinforcement learning for my future study in the field of robotics. I have seen that you only have 4 videos about RL. I am hungry for more of your videos. I found that your videos are easier to understand because you explain well. Please add more RL videos. Thank you 🙏

I would like to say thank you for the wonderful video. I want to learn reinforcement learning for my future study in the field of robotics. I have seen that you only have 4 videos about RL. I am hungry for more of your videos. I found that your videos are easier to understand because you explain well. Please add more RL videos. Thank you 🙏

So well explained

DPO main equation should be PPO main equation.

Exvelente explciacion!!!

Most intuitive explanation for QKV, as someone with only an elementary understanding of linear algebra.

It's kinda hard to remember all of these formulas and it's demotivating me from further learning.

I'm a little confused about one thing: the reward function, even in the Bradley-Terry model, is based on the human-given scores for individual context-prediction pairs, right? And πθ is the probability from the current iteration of the network, and πRef is the probability from the original, untuned network? So then after that "mathematical manipulation", how does the human-given set of scores become represented by the network's predictions all of a sudden?

Thank you for the wonderful video. Please add more practical example videos for the application of reinforcement learning.

noone of the videos I seen on this subject actually explain where the hell qkv values come from! its amazing people jump on making video while not understanding the concepts clearly! I guess youtube must pay a lot of money! But this video does a good job of explaining most of the things, it never does tell us where the actual qkv values come from, how do the embendings turn into them, and actually got things wrong in my oppinion. the q comes from embeddings that are multiplied by the wq, which is a weight and parameter in the model, but then the question is, where does wq wk wv come from???

how do you choose the number of features in the 2 matrices, i.e. how did you choose to have 2 features only?

Hey I know this 👦. He is my Maths teacher who don't only teach but make us visualize why we learn the topic and how will it useful in real world ❤

It‘s was just so clear. 😃

I love his teaching, he makes complex things seem simple.

Love your videos, please make more such videos on mathematical description of generative models such as GAN, Diffusion, etc.

We expect to describe wasserstein distance 😊

thank u

Thank you Luis. I'm sure I'll use this very soon.

That was intuitive as butter

Great video. One question I have, why would I use KL instead of CE? are there situations in which one would be more suitable than the other ?

why did you take average at 6:30 ?

Thanks. That was good except so elementary things explanation like logarithm manipulations (every who is interested in your video has already known elementary math).

most intuitive video on KL Divergence...loved it.

The number shown in cells (2,1) and (3,1) in the table (time 10:32) to calculate the probabilities of the sequences are typo mistake. Pls correct it . ua-cam.com/video/sjgZxuCm_8Q/v-deo.html

Thanks for posting this.

Wow!! Thank you!!! Finally I got it !

What a Morning surprise..lovely video

Thanks!

I've watched so many videos, read through so many websites, and asked so many questions to chatgpt about pca. I have to learn it as quick as possible for a research program. This is by far the best explanation I've seen!!

Why do you need Value neural network? Why can't you train the policy neural network alone? Is it because the value neural network allows to replace human evaluator and get more training samples for the policy network without need for human input?

here is prop up for a great channel.

Ohh I missed this.

Just discovered it. Phenomenally perfect balance between intuition and quantitative explanation.

dawg...what is this music lmaoo

2 Favorite teachers.

thanks really