Word Embedding and Word2Vec, Clearly Explained!!!

Thank you so much for this playlist! Got to learn a lot of things in a very clear manner. TRIPLE BAM!!!

the best video I saw about this topic so far. Great Content! Congrats!!

Thanks!

I admire your work a lot. Salute from Brazil.

Hi Josh! Thank you so much for the clear explanation! I'm just having trouble understanding why is it that we DON'T want to predict "abandon" but we are still predicting the weights that lead to it? Shouldn't it be that we WANT to predict "abandon", and the Negative Sampling selects a subset of words that we WANT TO PREDICT?

Hey Josh, i'm a brazilian student and i love to see your videos, it's such a good and fun to watch explanation of every one of the concepts, i just wanted to say thank you, cause in the last few months you made me smile beautiful in the middle of studying, so, thank you!!! (sorry for the bad english hahaha)

Hi Josh, thank you for your excellent work! Just discovered your videos and consuming like a pack of crisps. I was wondering about the desired output when using the skip-gram model. When we have a word as input, the desired output is to have all the words found within the window size on any sentence of the corpus activate to 1 at the same time on the output layer, right? It is not said explicitly but I guess it is the only way it can be.

Todaaay "guitar"
I ordered "guitar"
The-Illustrated-Guide-To-Machine-Learning by.... "guitar"

StatQueeeeest "guitar guitar"

Squash is cute <3

Thank you so much for these videos. It really helps with the visuals because I am dyslexic… Quadruple BAM!!!! lol 😊

Thanks for enlightening us Master.

The input : "great!"
What is the ouput ?

can you do this for Images?

Bro , i have my master degree in ML but trust me you explain it better than my teachers ❤❤❤
Big thanks

When I watched this,I have only one question which is why all the others failed to explain this if they are fully understood the concept?

Wow, Awesome. Thank you so much!

funny and very nicely explained.

I have a question? Are the number of outputs softmax generating at the end of word 2 Vec varying between 2 to 20? Thats why the numbers of params calculated as 3M × 100 × 2? If it were to predict probs for all 3M words, would it have been 3M × 100 × 3M?