Recurrent Neural Network (RNN) Tutorial | RNN LSTM Tutorial | Deep Learning Tutorial | Simplilearn

This Recurrent Neural Network tutorial will help you understand what is a neural network, what are the popular neural networks, why we need recurrent neural network, what is a recurrent neural network, how does a RNN work, what is vanishing and exploding gradient problem, what is LSTM and you will also see a use case implementation of LSTM (Long short term memory). Neural networks used in Deep Learning consists of different layers connected to each other and work on the structure and functions of the human brain. It learns from huge volumes of data and used complex algorithms to train a neural net. The recurrent neural network works on the principle of saving the output of a layer and feeding this back to the input in order to predict the output of the layer. Now lets deep dive into this video and understand what is RNN and how does it actually work.

Below topics are explained in this recurrent neural networks tutorial:
1. What is a neural network?
2. Popular neural networks?
3. Why recurrent neural network?
4. What is a recurrent neural network?
5. How does an RNN work?
6. Vanishing and exploding gradient problem
7. Long short term memory (LSTM)
8. Use case implementation of LSTM

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Do you have any questions on this topic? Please share your feedback in the comment section below and we'll have our experts answer it for you. Also, if you would like to have the dataset for implementing the use case shown in the video, please comment below and we will get back to you. Thanks watching the video. Cheers !!

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[mohamednedal]

Thanks for the great tutorial! A couple of questions please.
To establish a Multivariate Multi-Step LSTM Models - Multiple Input Multi-Step Output:
1. How can I modify this code to take, for instance, 3 inputs to forecast a different single output that depends on those 3 inputs?
2. How can I forecast multiple timesteps in the future without knowing the new inputs (because they're in the future)?

[AhmedAli-jzrg]

You cant use this code for forecasting since you concatenate the open of the train data set and the open of the test data set to get the predictions. We don't have the values of the future when we are forecasting.

[marcospaulojunior]

awesome video ! Just a hint, in 32:50 of the video you could just do: dataset_train[['Open', 'High']].values

[sammy]

Realy cool tutorial on rnn. Thank you for your work😍😍

[pedrojuniorzucatelli]

Congratulations for the Recurrent Neural Network (RNN) Tutorial. Then, I would like that you can help me to implement a code with LSTM to predict wind speed 6 hours ahead. The first 550 table lines would be for training and the remaining 194 for forecasting. I greatly appreciate the support. Thank you very much. Mr Pedro Jr

[Albert-fejx]

Excellent video. Thank you for offering it. at 12:45 discussing vanishing while slide is exploding...timing off.

[abhishekpandey]

Excellent Explanation, Thank you so much for this video, Is there any video regarding text generation using LSTM _ RNN

[bloom]

awesome content and explanation. Adding a sense of humour while teaching for 1 hour non-stop is excellent. Good job sir

[kartikgarasia]

well nice explanation but in stock section there is a problem called "time lag" which returns last close values from the given sequence.

[melakuhailelikka]

Thanks for your much helpful demonstration

[ekehopenkiruka]

Thank you so much for this video that cleared my brain that was about to blow trying to understand RNN and LSTM application in Data analysis.

Is there any video where RNN-LSTM was used on KDDCup99 dataset for anomaly detection

[brownboldtype]

please provide a tutorial for multivariate inputs also

[mohsenali]

Hi
Thank you for this great video,
Can give the dataset to implement the use case?

[BrownDogIsInTheHouse]

Thank you for an excellent tutorial and also for using a practical real life example. I was going round in circles trying to figure out what was the last parameter as one when we were reshaping in numpy array and now I understand it is just an indicator that the number of parameters is now ending. In this example you have used three layers of lstm ? Is there any information on how the learning process and also the outcome would vary when we used different layers of Lstm

[SannidhiPHebbar]

slides are not in sync with speech..but a good explanation

[BrownDogIsInTheHouse]

Also, you have mentioned that batch size only pertains to the number of items that would be loaded into the Ram but at other places I am seeing that page size actually defines how much data the neural network could see in one shot. Although I must confess I don't understand the Latter definition

[luisbianucci]

Excellent explanation. It is possible to build a recurrent neural network to use it in forex, based on indicators: MACD, cci, RSI, bb, Ichimoku?

[aakashv]

Excellent tutorial and I really liked it .But I have confused between C(t-1) and h(t-1) . C(t-1) is Cell state and h(t-1) is output of first cell right? Do we use the both in the next timestep ?Because I found only h(t-1) is used in the 2nd timestep and C(t-1) hasn't been used .Or have we added the cell state t-1 with t ?