I am very glad to find your channel. You make this topic for a beginner as me so understandable. Hopefully a following to write this in a python code. Many thanks.
Kash mene phly prh liya hota aj paper hai aur apki videos dekh kr itna achay sy smjh arha hai kiya hi bolun apko...Thank u so much for providing us knowledge like this
remarkable explanation , keep bringing good content. just one little suggestion , try to write keypoints on board while explaining so that we can copy and it will help us while revising
@@pothusandeepkomal1006 I am goong to upload those videos, but it will take some time. Right now, i am busy with my exams, so will upload when i get time. 😇
For time series data (there are around 60 input variables) and there are two outputs variables. Which deep learning model would be best LSTM ? Here accuracy matters. For learning time does not matter. For 2 output variables how to design LSTM model ?
Hello bhaiya, thanks for the informative contents. But can you please explain me why you are saying ft (forget gate) is a matrix. From the formula, it just an output of a sigmoid function, which I think a scalar value for each time step. Please explain this part. 🙏🙏
Hi , i hope one day you explain transformers because ur explainations is great ,but i really need your help about something because i really got tired of searching about the answer i got stucked on something and seems like no one helps so i hope you help me , now i studied about LSTM and Bi-LSTM and i understood them well , but i read some blogs said that bi directional LSTM good for sentiment analysis and time series so i really got confused about it , How it could be useful !!!! it will be useful if my current prediction depends on what happens in the future ,so how it could be usefull in sentiment analysis if i already will predict my final output in the last word so there is no future because i stand in the last , i know it could be usefull in some applications like name entity recognation because the type of the output is (many) so maybe my current output depends on what is happend in the future i really hope to help me because i didn't find any reason after 2 hours of searching in google
Hi, bidirectional LSTM looks at all the words appearing in an input sentence, from both directions, front to back and back to front. So, you can always assume that bidirectional LSTM can be useful for any application involving sentences as input. I don’t have any more reason for this, for now.
You are better than my professor, thank you
Thank you for these awesome classes brother
I think u deserve much more than 27k subscribers man. I totally got it after watching this playlist
I am very glad to find your channel. You make this topic for a beginner as me so understandable.
Hopefully a following to write this in a python code. Many thanks.
from a man from Egypt send big thanks to you ❤❤
Your welcome
best explanation with no faltu pnchyti and made the topic crystal clear
Thanks! 😁
Kash mene phly prh liya hota aj paper hai aur apki videos dekh kr itna achay sy smjh arha hai kiya hi bolun apko...Thank u so much for providing us knowledge like this
Hi, I am elated from your words. Glad it was helpful.
Very easy to understand. You are better than many prof. Thanks bro.
Thousand of thanks for your effort to make this video tutorial
Happy that it was valuable. Thanks for the compliment!
brother u made learning machine learning so easy. When i got money i will be sure to show my thanks
Best explanation...... It helped me for endsem exam...thank u sir.....keep creating ... let's get riding🙂🙂
u made my day..thnx lot
My pleasure 😊
That was a good binge man. Hopefully attention/transformers will be covered too!
Thanks for the suggestion... I will try to cover those too
remarkable explanation , keep bringing good content. just one little suggestion , try to write keypoints on board while explaining so that we can copy and it will help us while revising
I am so happy now. Thanks!
😂
Thank you so much! I was so lost and you really helped me get to grips with what is going on
Excellent. Thank for this video and explaining complex concept like LSTM in very short and crisp video
Superb explanation brother.. thank you so much 😍.. I got very clear understand on LSTM and as well as RNN
Clear and concise explanation, thank you!
Very good explanation
thanks man …. very helpful … cheers !!!
Cheers!!
@@CodingLane could you please make videos on GRUs ,se2seq,Attention models, Transformers?
@@pothusandeepkomal1006 I am goong to upload those videos, but it will take some time. Right now, i am busy with my exams, so will upload when i get time. 😇
You're doing a great job bro ✌️❤️
Thank you so much 😊
Thank you for the detailed to the point explanation.
Very helpful because of mathematical explanation and summery in the last
love this video!!
You describe everything very well✌️
Great Explanation
Thank you
Worthy explanation!
Amazing- great work
thanks my dear bro
Great resources 🙌
Thanks
Good vdo
Pl use white marker 😊instead of red
you are the best thank youu
Thank you very much!
thanks a lot!
thank you for this video
I watched many videos for this topic but couldn't understand it. You made every point clear in a beautiful way 🫡
Tnx a lot
Thank you
5:00 the green formula ; shouldn't it be a sum of multiplication through 0~t instead of just a sum
What is Bc, Bf, Bi, Bo added everywhere ?? Is that bias ?
Biases or as we can say the Constants added in every function, indicating the margin of error.
For time series data (there are around 60 input variables) and there are two outputs variables. Which deep learning model would be best LSTM ? Here accuracy matters. For learning time does not matter. For 2 output variables how to design LSTM model ?
Hello bhaiya, thanks for the informative contents. But can you please explain me why you are saying ft (forget gate) is a matrix. From the formula, it just an output of a sigmoid function, which I think a scalar value for each time step. Please explain this part. 🙏🙏
te quiero mucho
At 8:45 you are wrong about matrix multiplication
Arigatto
Hi , i hope one day you explain transformers because ur explainations is great ,but i really need your help about something because i really got tired of searching about the answer
i got stucked on something and seems like no one helps so i hope you help me , now i studied about LSTM and Bi-LSTM and i understood them well , but i read some blogs said that bi directional LSTM good for sentiment analysis and time series so i really got confused about it , How it could be useful !!!! it will be useful if my current prediction depends on what happens in the future ,so how it could be usefull in sentiment analysis if i already will predict my final output in the last word so there is no future because i stand in the last , i know it could be usefull in some applications like name entity recognation because the type of the output is (many) so maybe my current output depends on what is happend in the future
i really hope to help me because i didn't find any reason after 2 hours of searching in google
Hi, bidirectional LSTM looks at all the words appearing in an input sentence, from both directions, front to back and back to front. So, you can always assume that bidirectional LSTM can be useful for any application involving sentences as input. I don’t have any more reason for this, for now.
@@CodingLane
So , is my question make sense ? or there's something i can not understood about the intuation for now ?
the summation formula is wrong! one product sum should be there along with plain summation, that will unfold the longer terms...
Hi! i want to learn text detection from images using RNN. Please if you can help ???
3:22 problem with RNN
❤❤❤
GRU???
Proably the worst video on lstm i have seen
Amm.... sure... All videos are different kind... sad to see this tutorial wasn't help to you :(