I have no idea!

Yeah you are right, I realized I made that mistake. I need to reshoot it.

Thank you Damien, and math_in_cantonese I'm in the middle of writing a short article discussing position encoding. Damien, feel proud that you are the first reference I quote in the article! I was just going crazy trying to nail the exact meaning of "i". In Damien's video it is clear he means "i" the dimension index, and the values shown with sin/cos match. But now I could not make any logic of this understanding with the equation formulation below: PE(pos,2i) = sin(pos/10000^2i/dmodel) PE(pos,2i+1) = cos(pos/10000^2i/dmodel) If see this as PE(pos, 0) referreing to the first column (column zero) and, say, PE(pos,5) as referring to the sixth column (column 5), with 5 = 2i+1 => i = (5-1)/2 = 2. So "i" is more like the index of a (sin,cos) pair of dimensions. Its range is d_model/2. The original sin (😄, pun intended) is in the Attention is all you need. There they simply state: > where pos is the position and i is the dimension This is wrong, it seems, 2i and 2i+1 are the dimensions. In any case big thank you Damien, I have watched, many of your videos. They are quite useful in ramping me up on LLM and the rest. Merci beaucoup Alain

The sine and cosine functions provide smooth and continuous representations, which help in learning the relative positions effectively. For example, the encoding for positions k and k+1 will be similar, reflecting their proximity in the sequence. The frequency-based sinusoidal functions allow the encoding to generalize to sequences of arbitrary length without needing to re-learn positional information for different sequence lengths. The model can understand relative positions beyond the length of sequences seen during training. The combination of sine and cosine functions ensures that each position has a unique encoding. The orthogonality property of these functions helps in distinguishing between different positions effectively, even for long sequences. The different frequencies used in the positional encodings allow the model to capture both short-term and long-term dependencies within the sequence. Higher frequency components help in understanding local relationships, while lower frequency components help in capturing global structures. Also, sinusoidal functions are differentiable, which is crucial for backpropagation during training. This ensures that the model can learn to use the positional encodings effectively through gradient-based optimization methods.

I make them myself. Takes me most of my time!

@@TheMLTechLead Respect! What do you use to make them?

@@do-yeounlee7202 I use canva.com

Glad it was helpful!

How long have you lived in Europe and what countries exactly?

Thanks, will do!

Thank you!

I am not sure I understand the question.

I cannot share the slide but you can see the diagrams in my newsletter: newsletter.theaiedge.io/p/understanding-the-self-attention

I am liking reading that!

I am glad to read that!

Well I do!

It is coming! It will take time

My accent + my speaking skills are my weaknesses. Working on it and I think I am improving!

@@TheMLTechLead Thanks for your reply but absolutely no apology necessary!! I think it is an excellent video and helpful information. Much appreciation for posting. I am a professor in a business school and always looking for insights into how to teach the technical side of technology in the context of business. Your explanation has been very helpful.

Tree-based method can naturally be used to measure Shapley values without approximation: shap.readthedocs.io/en/latest/tabular_examples.html

So we have an ensemble of trees F that predicts y such that F(x) = \hat{y}. The error is y - F(x) = e. We want to add a tree that predicts the error T(x) = \hat{e} = e + error = y - F(x) + error. Therefore F(x) + T(x) = y + error

Why

“Give me the exam solutions pls”