Hello all.
So we are going to continue the discussion with respect to our LSTM and GRU.
And in this videos and in the upcoming series of video, we are going to develop some amazing end to
end projects.
Uh, in this particular project, we are going to probably talk about the next word prediction, you
know, using LSTM.
So whatever concepts we have actually learned from LSTM, we by using those concepts, we are going
to see how in a practical way, we can probably go ahead and implement this specific project that is
called as next word prediction using LSTM.
And after completing LSTM, we will see what are the step by step mechanism to probably solve this particular
problem.
Problem statement.
And then we'll also apply the same problem statement with GRU.
Okay.
So uh let's go ahead and let's see the project overview.
So here you can see this project aims to develop a deep learning model for predicting the next word
in a sequence of words.
The model is built using long short term memory.
Uh, long short term networks, which are well suited for sequence prediction tasks.
The project includes the following steps.
Data collection.
We use the text of Shakespeare Hamlet okay.
As our data set, we will specifically use this particular data set.
This rich, complex text provides a good challenge for our model because again, this particular uh,
if you probably go ahead and see this particular text.
Right.
Uh, it's not not just a normal English.
Right.
You know, it will be very difficult just to understand.
So we'll try to train our entire model with this particular data set.
Then we have data preprocessing and data preprocessing.
The text data is tokenized converted into sequence and padded padded to ensure uniform length.
So we will be seeing this data preprocessing technique how to perform it.
And we will also try to generate a pickle file out of it okay.
The sequences are then split into training and test sets.
Uh, then you have this model building, uh, where we'll be using an LSTM model is constructed with
an embedding layer, two LSTM layer and a dense output layer with a softmax activation function to predict
the probability of the next word.
Then we will go ahead and do the model training.
The model is trained using the prepared sequence, with early stopping implemented to prevent overfitting.
Early stopping monitors the validation loss and stop monitoring when the loss stops improving.
Okay, then you have this model evaluation.
The model is evaluated using a set of example sentences to test its ability to predict the next word
accurately.
So finally, once we do all this important steps, then we are going to deploy it by using a Streamlit
web application.
Okay.
And in this, uh, Streamlit web application, what we will do is that we'll will allow users to input
a sequence of words and then get the predicted next word in a real time.
Okay, so all these things we will specifically be doing again, uh, this will be really an amazing
project altogether.
We will be developing this, uh, in such a way that, uh, we will be able to understand this completely
step by step.
First of all, we will go ahead and experiment it.
So here you will be able to see that I've actually go, uh, went ahead and created a LSTM RNN folder
already in our requirement dot txt whatever libraries we wanted.
Uh, we will use this.
Okay.
So uh, first of all what I will do is that since I'm working in the same environment file, uh, I
will just go ahead and install this pip install minus our requirement dot txt.
But before that, um, I would also like to have one more very important library, which is specifically
called as, uh, if I probably go ahead and see over here in the requirement dot txt, I have to use
one more library which is called as uh nltk.
Okay, now this nltk uh library will be important.
Uh, if I really want to download this specific data set, the data set is basically present over there.
Now quickly let me just go ahead and do the installation minus our requirement dot txt.
Please do or don't forget to make the environment variable.
Uh how we have created for this v and v.
Right.
So this is the first step.
We have understood about the problem statement, what we are going to do.
And now we have also went ahead and installed the NLTK library.
Okay.
Now in our next video we will start step by step.
First of all, we will try to complete data collection and data preprocessing.
We'll save a pickle file and then we will move towards model building.
So I hope you understood the problem statement.
I hope uh, I have also given you an idea how to probably create this particular folder, and in the
same V and V environment will work where we have actually worked with a simple RNA in classification.
So please make sure that you also use the requirement dot txt what we have given in the end classification.
Only one library you need to install that is the NLTK.
Okay, so yes, this was it.
I will see you all in the next video where we will be starting our data collection phase.
So I will see you all in the next video.
Thank you.