4-3-2. Sentiment Prediction RNN

Welcome back. So now I’m going to go through my solutions to the exercises I had you do before. So you might have come here because you were having difficulties actually implementing that stuff or maybe you just want to see how I did it. You probably ended up doing it differently than me, which … Read more

(speaker) So here I am calculating the output. So like I said before, we’re only really concerned with the final output, which is what we’re going to use to predict our sentiment. So we just need to grab the last one and we can do that using outputs. So this is saying, give us all … Read more

(speaker) Array. So now we’re going to build the graph. So first thing that we need to do is define our hyperparameters. The first one is the LSTM size. So this is the number of units in the hidden layers in the LSTM cells. So LSTM cells actually have four different network layers in them. … Read more

The next thing to do is create our training, validation, and test sets. This is something you’re probably going to have to do for every network you build, so it’s good practice to do this for every data set you’re using. And I found that a lot of, basically like every different data set has … Read more

So now that we have all the words, what we need to do is we need to encode all of our views as integers. So we’re going to pass in the reviews where every word is an integer and that’s going to go into our embedding layer. So the first step, which I’m going to … Read more

(instructor) Hello again, everyone. So this week we are going to be talking about Sentiment Analysis with Recurrent Neural Network. So hopefully this will give you some more insight, and some more understanding about how recurrent neural networks work. So you’ve seen a lot of this before. We’re going to be using the same data … Read more