LSTM explained simply | LSTM explained | LSTM explained with example.

LSTM explained simply | LSTM explained | LSTM explained with an example
#lstm #machinelearning #deeplearning #ai
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Topics for this video:
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LSTM explained,
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lstm for stock prediction,
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[@rajendrabhise4427]

This is the first video I encountered on LSTM subject and I dont think, I need to watch other videos to understand LSTM any more. What a clear and straight to the point lecture.

[@UnfoldDataScience]

You words are precious to me. Thanks a lot

[@deepakdodeja4663]

This is a well-researched, well-designed, well-explained video I would say. Thanks a lot for efficiently explaining LSTM in just 30 minutes.

[@msbrdmr]

This was the most understanding tutorial I've ever seen about LSTM. Keep going. You are awesome.

[@shanjidabduhalim6146]

What a clear and straight to the point lecture. Thanks Prof!

[@vetrivelmurugan4937]

Super, Excellent,I got Very clear idea about LSTM🎉😊

[@youssefbechara8809]

I have to say mr.aman you've became my favourite deep learning teacher, your ability to teach HARD and complex maths in simple real-life examples is amazing! Not to mention the other youtubers teach us like robots they keep saying hard words, however you really make us feel like its a conversation!! It's really apparent how much professional and passionate you are about your work. Thank you so much❤!

[@UnfoldDataScience]

All the best

[@Mimimoon2024]

thanku prof, now am confidante enough about what happening in LSTM cells .its well clear even the math behind it very clear and well explained . thanku prof for this course.

[@nagarajtrivedi610]

Very well explained Aman. All these days I was not clear about how it retains information for short duration and long duration. I was also questioning myself how lstm predicts new words in a sequence. Today it has become clear to me. Thank you again.

[@dheerajp3024]

For the question asked at 18:38,
The range of Sigmoid function is [0,1] and Tanh function is [-1,1].
During Backpropagation, the partial derivative of Sigmoid function is much closer to zero when compared to the partial derivative of Tanh function. In longer-range networks the partial derivatives of Sigmoid function decreases to zero and can cause Vanishing gradients problem. But the partial derivative of Tanh is closer to one, hence the advantage and this solves the problem of Vanishing gradients. But we need to keep in mind that LSTM can still suffer from the problem of Exploding gradients, hence we use techniques like Gradient Clipping and Batch and Layer Normalization.
I hope it answers the question.

[@ShivaniChauhan-g8t]

thank you sir for this explanation of LSTM, made easy and understandable in few mins.

[@renus4898]

Hai Aman, No words to say... Simply superb! Excellent topic selection, explanation, and presentation. Please continue your journey; it is so helpful.

[@omarabubakarosman2791]

very explicit presentation. We are very grateful for breaking down this complex concept of deep learning.

[@UnfoldDataScience]

Thanks Omar. Pls share with friends

[@SelfBuiltWealth]

No bullshit this is the best explaination on youtube ❤❤pls keep helping us

[@UnfoldDataScience]

Thanks a lot.

[@Okive-green]

thank you sir for this brief detailed video. it really helps me to get the idea about lstm

[@UnfoldDataScience]

Thanks Pankaj.

[@barwalgayatri4655]

Too Good Amamn very very easy to undersatnd

[@UnfoldDataScience]

It's my pleasure

[@vimalashekar-c8c]

I like your style of teaching. Great job!

[@houssam0017]

Thanks alot. Well explained

[@samadhanpawar6554]

Amazing explanation very clear to understand keep up the good work
Make a video on GRU , Transformers, attention mechanism,encoder and decoder also😊

[@UnfoldDataScience]

Thanks a lot. Your words are my treasure

[@Tatanajafi]

Best description. Thank you.

[@samiashah7914]

Zabardast, very well explained. ❤ Thank you Sir

[@vimu-frm-slm]

Hi Aman, Thanks for your video..I understand the Vanishing Gradient problem, where small gradients are back propagated to update the weights. If the gradient is small, the update to the weights will be even smaller, which will reduce the learning rate of the model and lead to poor performance.
I also understand the LSTM model, which has long-term memory, short-term memory, a forget gate, an input gate, and an output gate. The thing I don’t understand is how the LSTM fixes the vanishing gradient problem during back propagation. The gradient can still be small even when using an LSTM, and when it is back propagated, the weight updates and learning rate will still be impacted.
I understand how LSTM is used & helps in forward propagation.. How it helps in back propagation? Please make a video explaining that.. Your help is much appreciated.. Thanks again

[@agbershimaemmanuel-ci6mz]

Thank you Amar for this interesting lecture

[@UnfoldDataScience]

Welcome. Keep learning

[@nimeshraijada5844]

You have excellent teaching skills 👍

[@UnfoldDataScience]

Thanks a lot.

[@revanthisbnimmagadda]

Well explained Aman , like the video on LSTM

[@swapnil2881]

Excellent I watch so many video but not clear concept but today video very helpful

[@UnfoldDataScience]

Thanks Swapnil.

[@TheMato1112]

Thank you very much. Finally, I understand. Well Done

[@UnfoldDataScience]

Most welcome. Pls share with friends as well

[@rosie-5h]

wow such a hard topic still felt really simple thank you sir for explaining it very nicely

[@UnfoldDataScience]

Thanks for watching

[@slickjunt]

Keep up the great work !

[@UnfoldDataScience]

Thanks, will do!

[@raheemasghar2383]

Great effort Aman

[@sunilkumarsam150]

Best explanation ever!👍

[@UnfoldDataScience]

Glad it was helpful!

[@PriyaBeram13]

Great Explanation Thank you sir! Could you tell me use of tanh in input gate?

[@UnfoldDataScience]

Yes surely we can have a separate video on it

[@prateekbhadauria7004]

Nice and brief supereb explanation, thank you for spreading your knowledge.

[@UnfoldDataScience]

Thank you Prateek

[@shine_through_darkness]

Thank you brother , your channel is really good

[@NileshPatil-pl2fj]

I liked the video...very nice explanation

[@UnfoldDataScience]

Thanks Nilesh

[@veenajain]

Awesome content Aman

[@vincentdey4313]

Well Explained . Great Job

[@princekhunt1]

Nice explanation 👍

[@zenithmacwan]

Excellent explanination! Thank you sir!

[@UnfoldDataScience]

Welcome

[@TheSerbes]

I want to make a parameter selection in lstm. I will remove unnecessary parameters. Do you have a video on how I can do this?

[@divyaharshad9985]

v good content! explained lucidly

[@Gezahegnt2000]

Nice Tutorial thanks

[@Hunger_Minds]

Aman Sir having a query regarding about LSTM architecture.
In how many iteration will model to understand which one word is important & which one is not?..

[@RafaelRivetti]

In the MLP network, data from independent variables from date t are used to predict a future value t+n. In the LSTM network, instead of using only data from time t of the independent variables, it uses data from time t, t-1, t-2, ..., t-n as desired by the programmer, and after that, generates the prediction for a future time t+n? Is this reasoning correct? Thank you very much!

[@shubhrashrivastava9510]

What is the difference between the final output ot and ht??? Explain in detail plz...thanks in advance

[@preethibaligar6766]

And just like that magic of understanding happened!

[@spoc.mnmjecspringboardmnmjec]

Good sir

[@danielfiadjoe9312]

Thank you. This is very clear.

[@UnfoldDataScience]

Welcome Daniel.

[@danielfiadjoe9312]

Do you have any video on Stock prediction using LSTM ? @@UnfoldDataScience

[@sunilkumarsam150]

sir, can you make one video of the implementation and research paper writing effectively in the field of NLP, ML, and DL.

[@UnfoldDataScience]

Sure.

[@knik8]

Waiting....

[@Hiisbnxkskkwl]

Sir badiya ekdum chutkiyo mae master Kara Diya 😂

[@adityarajiv6346]

thanks it was really helpful!.

[@KhairulMia-tr2jv]

good tutorial on LSTM which game me good idea.

[@UnfoldDataScience]

Glad you liked it!

[@aiddee-p2n]

Simply amazing

[@kavyanagesh8304]

You're THE BEST! Thank you.

[@UnfoldDataScience]

Thanks a lot. pls share with friends as well

[@prateekkumar2740]

Great content Aman. Could you please consider time series problem solving using LSTM. Thanks

[@MayankArya-jp7dq]

Thanks Sir :)

[@tehreemqasim2204]

excellent tutorial thank you

[@red_righthand-o4x]

great brother

[@aliyuabubakarmusa946]

The video is very interesting

[@UnfoldDataScience]

Thanks a lot

[@BhanusriAndhavarapu-xc1xb]

could you please explain timeseries data

[@selvimurali-o2s]

Nice explanation.thank u

[@casuresh7081]

Good Explanation. Thanks

[@ashwinijalla3504]

Please do series on complete learning of Generative AI concepts

[@lathusree]

Hi Aman can you help explain bus time arrival prediction using LSTM.

[@malaykhare1006]

amazing

[@AIandSunil]

Excellent explanation sir❤ thank you

[@UnfoldDataScience]

Thanks Sunil.

[@akshitacharak4385]

Thnkyou so much👍🏼

[@Amin-ue2dz]

you are amazing.

[@IT-pz8yz]

Great explanation ,thank you

[@UnfoldDataScience]

Most welcome. Pls share with friends as well

[@dhirajpatil6776]

Sir Please Can you provide written notes of this video means what you have explain in this video thats all I want written in words. Thank you

[@milindankur]

Great explanation! Thank you!

[@UnfoldDataScience]

Thanks Ankur.

[@muhammedthayyib9202]

GREAT work

[@UnfoldDataScience]

Thanks a lot. Hope you u r doing good.

[@sg28011]

How will forget gate will know a particular word is irrelevant or of less importance?

[@sg28011]

Got the answer as the video progressed

[@parsasakhi1505]

awesome! Thank you

[@afn8370]

thankyou man

[@rahulraizada7001]

What is different between tanh and relu?

[@chirumadderla8129]

can you please consider the eusecase of weather forecasting

[@UnfoldDataScience]

Sure, next video

[@jawaherabdulwahabfadhil2329]

Thank u sir

[@rahulraizada7001]

sigmoid lies between 0 and 1. whereas tanh lies between 1 and -1.

[@spoc.mnmjecspringboardmnmjec]

Nice sir

[@UnfoldDataScience]

Thanks and welcome

[@tishachhabra3369]

Best👍

[@UnfoldDataScience]

Thanks for watching.

[@chirumadderla8129]

Super stuff

[@UnfoldDataScience]

Thanks

[@bakyt_yrysov]

🔥🔥🔥

[@mohitchouksey6707]

Sir how many people got job from ur course?

[@hassanarshad2687]

🤯🤯🤯🤯🤯🤯

[@Mimimoon2024]

u know prof i bought a bunch of courses related to this , believe me they were not clear as much as your course.

[@KumR]

why sigma and why tanh?

[@UnfoldDataScience]

I can answer but I advise you to google it :)

[@allinteli]

sir please hindi mein video banaoo

[@siddheshmhatre2811]

Finally god found

[@_Channel_X]

This is known as "simple explanation" in real sense