I agree - I find the particular tone and pitch he uses when saying that to be painful (it literally hurts my ears). The transition from learning to review is a good idea; the execution can be improved.
I don't think that the issue is computers only understanding numbers. Even numbers are not directly understood by computers and need to be represented via combinations of "1"s and "0"s. Perhaps the issue is more related to the fact that neural networks and similar models can't directly deal with non-numerical data, and hence the need for a numerical representation before any training can take place. You make a great point about the higher computational cost of using natural or default numerical representations for items like words and images, which explains the need for an 'embedded' representation.
Thanks for video, you explain things in different difficulty level, that works. The quize and stuff is not working, for me breaks the flow of the content.
Can you please make a video that showcases how we can generate custom word embedding on a custom dataset from scratch? Without using anything pre-built? Say IMDb dataset? and then later load them to train a classification model?
Love the format!
The quizzes, visualizations and summary is awesome.
Thank you!
Thanks a lot Ajay for such amazing informational content. Just STOP saying the awkward QUIZZZ time.. whole focus /mood goes away
It's annoying, but it also gives a break between sections that would otherwise blue together.
I agree - I find the particular tone and pitch he uses when saying that to be painful (it literally hurts my ears). The transition from learning to review is a good idea; the execution can be improved.
I very much enjoyed every video you made till now. Your explanation has always been extraordinary, but please stop saying "Quiz Time" 😂😂
I don't think that the issue is computers only understanding numbers. Even numbers are not directly understood by computers and need to be represented via combinations of "1"s and "0"s. Perhaps the issue is more related to the fact that neural networks and similar models can't directly deal with non-numerical data, and hence the need for a numerical representation before any training can take place. You make a great point about the higher computational cost of using natural or default numerical representations for items like words and images, which explains the need for an 'embedded' representation.
Great video! Thank you so much! It makes things easier for someone who just started learning ML.
Genius presentation! Thanks! Keep up your excellent work!
Very useful and great explanation! Thank you so much!
my method to learn new words in vocab is actually train the pretrained model using transformers
5:06 techically both B and C are correct here. I guess i would say C is the primary and B is a nice (but necessary) sode benefit.
You failed sir! There is no technically.
The answer is C. Understand the question t...technically.
Thanks for video, you explain things in different difficulty level, that works.
The quize and stuff is not working, for me breaks the flow of the content.
this is very good. thank you!
Fun! Thank you!
can you prepare a video for Double Q-Learning Network
and Dueling Double Q-Learning Network
please
Can you please make a video that showcases how we can generate custom word embedding on a custom dataset from scratch? Without using anything pre-built? Say IMDb dataset? and then later load them to train a classification model?
Hi I have an id about an ai based podcast . Here I want to create ai as the main frame of the podcast . Can you guide me on this ?
C, B, A
Ding ding ding! I agree with your answers!
4:30
What was this??😂
Why it’s everyone’s favorite time Quiiiiiz Timmmmmmee, of course!
@@CodeEmporium yen Anna
C, B, A