Positional Encoding in Transformer Neural Networks Explained

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Positional Encoding! Let's dig into it

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TIMSTAMPS
0:00 Transformer Overview
2:23 Transformer Architecture Deep Dive
5:11 Positional Encoding
7:25 Code Breakdown
11:11 Final Coded Class

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CodeEmporium

Hands down!! You have put in sincere effort in explaining crucial concepts in Transformers.
Kudos to you! Wishing you the best !!

sabzimatic

Really enjoying this Transformer series.

wryltxw

Man you are awesome! I thought transformers were too hard and needed too much effort to understand.
While I was willing to put that much effort, your playlist has been extraordinarily useful to me.

Thank you! I subscribed

becayebalde

Attention is all you need! cit

your tutorials are gold, thank you

XCxPTB

Thanks for detailed videos on Transformer concepst!

shauryai

At time 6:24 reason 1 (periodicity) for positional encoding was under-specified, hence needed more clarity where it was mentioned that a word pays attention to other words (farther apart) in the sentence using periodicity property of sine and cosine function in order to make the solution tractable? Is it mentioned in some papers or can you cite this. Thanks.

mihirchauhan

One of the great explanation ajay, happy to see kannada words here ! . Look forward for more videos like this :-)
Kudos ! Great work ....

PravsAI

Most brilliant and simple to understand video

pizzaeater

One of the best series for transformers😄

SanjithKumar-xfsg

Dude these videos are so nice. Starting my masters thesis on a transformer-based topic soon and this is really helping me learn the basics

XNexezX

Thanks for the great video! Loving this series!

judedavis

I liked you already, now you are a kannadiga and i like you more.

lexingtonjackson

Theoratically what does it mean to add embedding vector and positional vector ?

ThinAirElon

thanks for the information in this video however i think i have a miss understanding,
you said before that before the vocabs are going into the embedding victor which is like a bag of related word together in a box,
but in the start of this video you said at first the words has done into a one hot encoder then passed to the positional encoding so what i want to know know is which is the scenarios is the right:
1- we take the word and search it into the embedding space then pass it into the positional encoder
2- we take the word and do it a one hot encoder then send it to the positional encoder

aliwaleed

i love your shit man, this was so usefull i actually understood this ml shit and now can be elon musk up in this llm shit

nawaelgiza

Kannada abhimanige kannidigana namaskara.. Nimma gyana bhandarakke namana.

ShivarajKarki

Clear explanation. If I want to use transformer for time series and the time is not evenly changing, there is irregularities of time points. How could I positional encoding of these time into transformer?

caiyu

You video are useful for me, Congratulation for excellent works. But I suggest you demonstrate a real video in multivariate time series forecasting or classification.

sangabahati

In the code on the final class, position is 1 to max sequence length....Which include both even and odd...I think we use cos for odd and sin for even..Why all the position are pass which mean 1 to max sequence length including even are pass in cos and odd are pass in sin.

convolutionalnn

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