Tutorial 34- LSTM Recurrent Neural Network In Depth Intuition

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.

confusing

Sir 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)?

Hi , 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

Excellent sir

Excellent..

Finest explanation of such a difficult topic, hats off!! 🫡

You disappointed us

Паша Техник совсем плох, индусом стал, нейронками занялся

Good Copy of Rohan's research. I see Most of materials are from TSAI

from which book you are teaching krish

nice! simple explanations.... much appreciable Sir

sir 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

Please go back to your whiteboard. You're amazing with whiteboard and marker!

Wonderful Explanation!

Sigmoid 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].

Thanks sir 🌸🙏

So 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?

amazing explanation sir..many thanks

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Sir, 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

I was really strugling to understand the core concept of LSTM. This really helped me. Thank you very much,,Also the blog is really awesome..

What happens to the -1 values of tanh and sigmoid cross product when the information is added to cell state in lstm?

the best explanation as usual,, thank you so much for your effort.

LSTM accepts input of variable size?? Or padding is required to make all input of same size?

Thank you, sir! It's great content and I'm almost following your NLP playlist.

Me 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

Can you please make a video on how to combine two deep learning model which are trained on different dataset

This is the best explanation on LSTM.. really thanks

is the video for the different types of LSTM skipped ?

I 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.

Day of Recording of this video is the day when the LOCKDOWN started !!!!!!!!

@krish naik wonderfull explanation

Thanks Krish

Hi krish ,
In lstm don't we have back propagation and weight updation ? if yes, why?

Amazing explanation, you made it very simple and clear

Hi, 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?

hello Krish can you explain Conv-LSTM with one sample data and difference with LSTM and time distributed concept of LSTM?

Mar-24-2021

Can you please make a video on GAN as well?

Great

How can we do extractive summarisation in BERT??