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Recurrent Neural Network | Forward Propagation | Architecture

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A recurrent neural network (RNN) is a class of artificial neural networks where connections between nodes can create a cycle, allowing output from some nodes to affect subsequent input to the same nodes. This allows it to exhibit temporal dynamic behavior.
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⌚Time Stamps⌚
00:00 - Intro
00:44 - Why RNNs?
04:20 - Data for RNN
09:54 - How RNN works?
22:50 - Code Example
25:12 - RNN Forward Propagation
36:59 - Simplified Representation

Опубликовано:

18 авг 2024

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Комментарии : 127

@Sidcr07 Год назад

Sir plz dont leave this playlist incomplete, u can't even imagine how this playlist has become a blessing for many, it's my heartedly request, and i m sure I m not alone in requesting the same, there are many others also, plz do update this playlist also, I have given up many paid courses to study from your masterpiece videos, plzz sir it's a humble request 🙏🙏🙏🙏

@campusx-official Год назад

Nai chor rahe hai bhai. Was travelling this weekend so could not upload. Will upload the next video tomorrow.

@Sidcr07 Год назад

@@campusx-official thnks a lot sir, for everything..!

@smruti_06240 Год назад

@@campusx-official 🥲why r u not uploading next video ??

@geekyprogrammer4831 Год назад

I gave up on paid courses too to follow this channel.

@endoumamoru3835 Год назад

@@campusx-official Sir,baaki videos kb aaenge. Please Sir unhe bhi upload krden

@alkalinebase Год назад

It breaks my heart how much shadow banned you are. I literally have to search you by typing the entire thing because this doesn't show up in the first 20 videos or so. You are the best teacher ever. I can't thank you enough sir!

@user-vk3th5re5c 5 месяцев назад

I have never ever commented on any video. Simplifying such concepts, you are the best teacher I have ever seen in my exploration of Data Science.

@ishujain9343 4 месяца назад

I have studied RNN from many other teachers, but after seeing this video, I really understand how it works. I now have the confidence to explain this topic to anyone. Thank you very much, sir.

@user-lr5jy1vk6w 3 месяца назад

This is hands down the best deep learning playlists out there, i am blindly following this because i just know sir will surely be covering all the content in depth and i don't have to go look for it anywhere else.

@anuradhabalasubramanian9845 Год назад

No words to express your teaching style Sir. So simplified and in depth.Hats off to your knowledge and presentation. God Bless You !

@AdityaKumar-fq2lq Год назад

Sir sare video dekh liye... Waoting for more video...plz sir jaldi jaldi bana digiye

@arunchechi1578 Год назад

Sir, you are genuinely the best data science teacher ... I feel sooooo blessed

@riyatiwari4767 8 месяцев назад

This playlist is amazing! One of the best for understanding deep learning. Thanks a lot! 🎉

@HappyHumbleHopefulHelpKey Месяц назад

This is really a blessing love your videos sir and how simply and gracefully you explain these topics.

@nallakrishna8796 Год назад

Sir, the way your explaining the things it's simply amazing I'm eagerly searching for learning of RNN MATHEMATICAL INTUITION finally when I watch your lecture I stopped searching for RNN .. you made the concept step by step more understandable.... please continuously upload these kind of concepts which will helpful for the upcoming students then can learn from you... sir it's my humble request......

@adityasrivastava78 Год назад

Beautiful explanation !!!! You are doing a great service to community !!!!

@aritradutta9538 6 месяцев назад

best ever explanation of RNN architecture. Thanks a ton

@rahuljha3686 8 месяцев назад

input format in RNN -> (timesteps, input_features) .... when we send the first word is sent, time = 1 "Movie was good" ... sent in as a tensor of (3, 5) where timesteps = 3 & input_features = 5 unfolding through time -> every node has an activation function which by default is tan h output of one cycle is fed back to the nodes in the next cycle

@aryangupta2051 28 дней назад

very simplified and logical lectures

@user-pd1gy8xh4y 10 месяцев назад

Awesome. Simple and detailed explanation of RNN.

@AniketSharma-gq2by Год назад

O complicated RNN, Bhaiya ji ne suru kar diya, itni khushi itni khushi🤡, time series 👀👀

@user-iv9jp9zv4v Месяц назад

This is exactly what I needed to understand RNN better. Thanks!

@74kumarrohit 7 месяцев назад

Simply the best. I have been recommending this channel to all

@ali75988 9 месяцев назад

Badshaho, hats off to you. cha gae ho.

@sunny739 4 месяца назад

Sir your tutorials are like : watch it once and you are done with understanding concept. later all we need is a revision for couple of times and done with the topic. Thank you for everything 🙏

@rog0079 Год назад

Hi, first of all, great content! I was looking forward to this playlist but I see it hasn't been updated since 6 months, I was hoping to learn more about LSTMs, GRUs, Transformers, BERT, GPT, LLMs etc through this playlist, I hope you continue this series soon, thanks anyway :)

@hriteshshanty252 Год назад

Yes even I want the full playlist

@chetankumarnaik9293 11 месяцев назад

You are just amazing. Please share video on LLM or make LLM's part of your mentorship program.

@sanchitdeepsingh9663 9 месяцев назад

thanks sir for simple explanation

@flakky626 10 месяцев назад

sir literally crying while typing this have tears in my eyes thanks a lot sir

@JourneytoWINover_PORN 2 месяца назад

kandha dun

@abdulqadar9580 11 месяцев назад

Thank u Sir for your great efforts.

@prashantm9856 8 месяцев назад

Most beautiful explanation sir ❤❤❤❤

@khoja110 Год назад

so beautiful and stay blessed

@rashidsiddiqui4502 Год назад

Thank u so much sir.

@dakshbhatnagar Год назад

Hey Bhai, The way you go into depth is great. Whatever deep learning I know is because of you only. Plus the hinglish hits different when learning new things. At least for me 😅

@afmfarhad2129 11 месяцев назад

Amazing explanation 💯💯

@vishnusit1 Год назад

great explanation..

@ItsPawan005 5 месяцев назад

Superbbbbb Explaination!!!! No need to use Recurrence (repeat video) once intrepreted neately

@NabidAlam360 2 месяца назад

So good! We want atleast 1M subscribers for you!

@tusharsingh1915 11 месяцев назад

Thankyou bhaiya for your hardwork. This series means a lot to me as it was so helpful in learning deep algorithms and everything. The content you provide was pretty commedable. virtual hugs to you. Love from Ghaziabad🙂❤

@theartistbhavik 2 месяца назад

Hats off for this level of simplification 🙌

@garimadhanania1853 3 месяца назад

god level content! simply amazing!

@deepshikhaagarwal4125 Год назад

Nitesh sir amazing!!!!

@sahuchiragshyamlal3684 Год назад

around 36, weight sharing, input dimension in keras, can store sequence of 10 time steps

@ahmadtalhaansari4456 Год назад

Learning new concepts. August 13, 2023😊

@mayankbhatt1308 2 месяца назад

Rnn thoda simplify kar diya?.....jo kuch smjh ni aa raha tha abhi tak , wo 1 baar mei poora hi smjha diya , godly explanation !

@VarunMalik-mo6mr 3 месяца назад

Thank you Nitish sir 💯

@deepshikhaagarwal4125 Год назад

Sir it would be really helpful if you can provide remaining videos..after watching your videos we have become addictive to your videos and not comfortable with any other videos

@ParthivShah 3 месяца назад

Thank You Sir.

@lifeshortsyt786 Месяц назад

Amazing explanation i wish I could have the notebook

@AbcdAbcd-ol5hn Год назад

Sir please please complete this playlist, thank you so much for the videos uploaded till now...

@pavangoyal6840 8 месяцев назад

wonderful

@ameyagurjar2576 4 месяца назад

ye aadmi legend hai ❤‍🔥❤‍🔥

@aiforeveryone Год назад

Amazing

@vishnusit1 Год назад

Thanks

@Naman_Bansal102 2 месяца назад

Great Video Sir

@ykvlogs4292 Год назад

really great effort sir , please complete this playlist

@rb4754 2 месяца назад

Super Amazing video...

@siddheshmadkaikar1645 4 месяца назад

Amazing explanation!

@AakashGoyal25 29 дней назад

Best explanation ever :)

@mohsinimam2048 Год назад

Thank you so much!

@rachitsingh4913 7 месяцев назад

The best❤

@murtuzakhan3655 Год назад

😭😭😭 how many thanks should we say to you ❤️❤️❤️❤️

@sandipansarkar9211 Год назад

finished watching and coding

@geekyprogrammer4831 Год назад

Hi Nitish. Amazing explaination! Breaking down such complex topic into simple bits! But please do upload backpropogation too for RNN.

@campusx-official Год назад

Next video wahi hai

@divyanshtuli Год назад

when will this playlist restart

@AdityaKumar-fq2lq Год назад

Sir very good video

@aadityaadyotshrivastava2030 6 месяцев назад

super

@rvdjc Год назад

Thank you for very clear explanation! Could you please tell me the book you usually follow to understand concepts like RNN?

@soumilyade1057 Год назад

I had already subscribed to the channel, but the video didn't appear in the search results 😑 because it's not in English? May be!

@messi0510 Год назад

11:35-->14:35 2 main difference between RNN and ANN

@curiousseeker3784 Год назад

you said something around 39:40 that final output layer (activated by softmax/ sigmoid) is used only for last timestamp, ig it's only for this RNN (sentiment analysis) , otherwise for each recurrent block, we may have used final output layers too alongwith hidden layers?

@sandipansarkar9211 Год назад

where is the link for this google colab

@HerambYt 8 месяцев назад

At t=2 why the input is connected only to 1 hidden layer it should be connected to all(3) hidden layer

@saurabhsingh1794 Год назад

Hi Nitish, Cab you upload videos on encoders, decoders, attention models and Transformers. btw your explanation is great. Good work .

@ali75988 9 месяцев назад

11:33 for anyone having problem that here input sizes are different. how would unequal be used as input. agley lecture main answer hai.

@rafibasha4145 Год назад

@17:37,please let me know why one neuron output should go to other neuron in same layer

@suhaspatil8682 Год назад

Sir How did you find the vectors like for movie 10000 ,for was 01000 etc etc

@geetakashyap411 3 месяца назад

Sir how u easily clarify all the difficult concepts

@yashjain6372 Год назад

best

@jooeeemusic7963 Год назад

Sir please continue This playlist sir. Pleasee

@narendraparmar1631 5 месяцев назад

Good Explanation, But how the past information affect the future once

@MurariMahaseth Год назад

Thank You Sir, can you please suggest book for more information ?

@aryanchandra6298 Год назад

sir update this playlist, we are eagerly waiting for the completion of this playlist

@parthdhore3757 Год назад

Could u plz update the ML Roadmap 2022 with the CNN and RNN videos

@mohsinimam2048 Год назад

Please bring videos on LSTM and BERT

@rafibasha4145 Год назад

Its been 8 days no video yet ,pls upload 2 videos per week and finish the playlist

@campusx-official Год назад

Upload the next video on Tuesday. Was travelling this week so could not shoot.

@rafibasha4145 Год назад

@@campusx-official ,thanks bhai pls finish Feature selection ,xgboost catboost from ML ,DL and advance NLP along with python ..if possible staart MLOPs as well

@navneetgupta4669 Год назад

Sir, when will we get the new video?

@muhammadishaq-co4fu Год назад

Sir, I hope you are feeling good. Sir please tell me how to have question answer with you plzzzzzz

@dattatreyanh6121 8 месяцев назад

Can u make a playlist cnn with lstm block

@debojitmandal8670 Год назад

Sir still I don't understand one thing when you explained the working this working still doesn't explain how your able to retain the context or semantic meaning it just tells how your able to maintain the order of words ime which word comes before which word but not it's importance of each word how much does each word has an impact on other words

@priyadarshichatterjee7933 Год назад

semantics aise retain ho rahe ki we are following this feedback approach har time step me unrolling k waqt we are using the output of the previous step as the input to the next step, ANN k ki tarah sare i/p eksath nhi jaa rahe network me, so agr hum input k order change karenge 1st step me so corresponding O1, O2, O3 sare hi change ho jayenge..... thus maintaining the semantics of the orginial word u input

@debojitmandal8670 Год назад

hi sir when will u upload videos on transfromers

@vishnusit1 Год назад

Hii Nitish, i appreciate the way you explain RNN , Most importanatly mathematical calculation..Can you suggest me book that explains deep learning in your style yes in your style..Bcoz i started reading many DL books but thier mathematical explanation was confusing and complex for me...Please respond

@campusx-official Год назад

May be Deep Learning with Python will suit your need

@ram_c Год назад

Please make tutorials on PINN. Thank you

@himanshuvora1980 9 месяцев назад

💚💚💚💚

@piyushpathak7311 Год назад

Pls upload next video sir

@ajitkumarpatel2048 Год назад

🙏

@ritesh_b Год назад

please upload xgboost video sir

@anirudh6543 4 месяца назад

Sir please provide these notes and code links.

@mohdadil2875 8 часов назад

is playlist ke complete notes h kisi ke paas ?

@vamshiharshik6495 Год назад

Can anyone please tell me that how to do train test split after one hot encoding as it increases target columns?

@akankshaporwal5566 Год назад

Is your problem solved????

@akankshaporwal5566 Год назад

Try labelencoder

@vamshiharshik6495 Год назад

@@akankshaporwal5566 no madam but label encoding is working but it has disadvantages right so I wanted to do one hot encoding but don't know how to proceed further