Комментарии:
Thank You sir for such videos, Just please arrange it in playlist or in your website in order to access it easily. Thank You so much.
Ответитьconfusing
ОтветитьSir why are we again applying sigmoid function in the input layer while we have already done in the Forget Date? what is the necessity of calculating i(t) again? isn't f(t) = i(t)?
ОтветитьHi , Thanks for your wonderful explanation,
In my opinion , this detailed video is more important for researcher rather than programmers want to use LSTM or RNN
Nice lecture sir. Plz, try to solve only one numerical example manually for at least one epoch sir. It will be helpful to understand lstm in depth. Thank you
ОтветитьExcellent sir
ОтветитьExcellent..
ОтветитьFinest explanation of such a difficult topic, hats off!! 🫡
ОтветитьYou disappointed us
ОтветитьПаша Техник совсем плох, индусом стал, нейронками занялся
ОтветитьGood Copy of Rohan's research. I see Most of materials are from TSAI
Ответитьfrom which book you are teaching krish
Ответитьnice! simple explanations.... much appreciable Sir
Ответитьsir g love ho gia ap sy main first time nlp smjny ki koshish kr raha tha q ky main ny final year my isy as a research work choose kia hy and sir your videos help me alot love you sir boht ziada
ОтветитьPlease go back to your whiteboard. You're amazing with whiteboard and marker!
ОтветитьWonderful Explanation!
ОтветитьSigmoid doesn't inherently converts real values to binary labels i.e. 0 or 1, instead it'll be range of real values between 0 to 1 (inclusive). The vectors at output of gates need NOT be something like [0 0 1 1] but can be, and most probably be, something like [0.122, 0.23, 0, 0.983].
ОтветитьThanks sir 🌸🙏
ОтветитьSo is it fair to say the forget gate decides "where the new word fits in the context" and hence the forgetting in the context and the input gate decides how the new word 'changes' the context, thereby altering the influence of the new word on the context?
Ответитьamazing explanation sir..many thanks
ОтветитьToo much advertisements😒😔
ОтветитьSir, are you possible image classify of folder wise move. I'm data operator forest Kaziranga National Park. Many photos trapped camera. Manual segregation is to very hard. Please help you
ОтветитьI was really strugling to understand the core concept of LSTM. This really helped me. Thank you very much,,Also the blog is really awesome..
ОтветитьWhat happens to the -1 values of tanh and sigmoid cross product when the information is added to cell state in lstm?
Ответитьthe best explanation as usual,, thank you so much for your effort.
ОтветитьLSTM accepts input of variable size?? Or padding is required to make all input of same size?
ОтветитьThank you, sir! It's great content and I'm almost following your NLP playlist.
ОтветитьMe watching other YT videos: Watch then like/dislike/do nothing
Me watching Krish sir's videos: First like then watch
Thank you so much for explaining so many things. I learnt complete practical ML/DL from your videos. A big thumbs up from my side. Definitely, I will share your channel to anyone who would want to dive into ML/DL/DS.
A small confusion in C t-1. How does Ct-1 differ from h t-1, if both are previous output
ОтветитьCan you please make a video on how to combine two deep learning model which are trained on different dataset
ОтветитьThis is the best explanation on LSTM.. really thanks
Ответитьis the video for the different types of LSTM skipped ?
ОтветитьI have recently been thinking of Data Science and Machine Learning, Krishna Naik's videos were very helpful in framing my decision. Thank you Krishna Naik.
ОтветитьDay of Recording of this video is the day when the LOCKDOWN started !!!!!!!!
Ответить@krish naik wonderfull explanation
ОтветитьThanks Krish
ОтветитьHi krish ,
In lstm don't we have back propagation and weight updation ? if yes, why?
Amazing explanation, you made it very simple and clear
ОтветитьHi, actually I don't understand why do we need to do the sigmoid part twice? Once for input and once for forget gate? Isn't it doing the same thing?
Ответитьhello Krish can you explain Conv-LSTM with one sample data and difference with LSTM and time distributed concept of LSTM?
ОтветитьMar-24-2021
ОтветитьCan you please make a video on GAN as well?
ОтветитьGreat
ОтветитьHow can we do extractive summarisation in BERT??
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