Recurrent Neural Network is a branch of Artificial Intelligence. There are many things to learn from this network, and it helps us understand the complexities of different types of algorithms. Today, we will discuss the answer to – What is Recurrent Neural Network or What is RNN?
Before understanding the Recurrent Neural Network, let’s go with a few basic examples to understand it.
In this case, you take two tweets and classify them into positive and negative. Like, “I like this new phone cover” is positive and “I don’t like this new phone cover” as negative. Hence, the output options are two here, positive and negative.
When you see images on a website, you will see they are captioned. It is a textual description of the image. The image’s input has a fixed size, but this doesn’t apply to the next part. It can vary to different lengths. So, the output of words is variable.
In language translation, the input and output can have varied lengths as output can have more or fewer words than the original input.
RNN Neural Network is used to map such outputs and inputs of varying lengths. Now, we will take a look at the architecture of an RNN algorithm.
To understand the answer to – What are Recurrent Neural Networks? – we will take a simple example of predictive text. There are three layers in a Recurrent Neural Network:
In this, the input sources from the user.
In this layer, the application gets processed. In a Deep Network, there can be multiple hidden layers.
In this, we receive the final result sent to the user.
Let’s consider the hidden layers are in great numbers because of its Deep Network. The input layer sends it to the first hidden layer that gets activated, and then these activations are passed on to the next hidden layer, and the process goes so on to give an output to the user.
Each hidden layer on an individual level has its weights and biases. Consequently, they operate independently from each other. Now, you have to understand the relationship between the inputs of each hidden layer.
If the weights and biases of the hidden layers are different, they cannot be put together. To accomplish that, we have to get the hidden layers in the same weights and biases.
RNN remembers the past applications of the input and output, and it gives out the result based on those past applications. There are a few formulae you must know to understand the memory of a Recurrent Neural Network.
To calculate current state:
ht = f(h(t-1) , xt)
In this ht = current state
h(t-1) = previous state and xt = input state.
To calculate output:
yt = Why ht
In this, yt = output and Why = weight of the output layer.
This entire process of input, hidden layer, and output is called Training an RNN.
There are many different types of Recurrent Neural Networks for various RNN applications.
The binary system had shown signals between many neurons when initiated by McCulloch and Pitts’ model in 1943. It explains the logic systems and short-term memory traces. The equation used by the network is –
The concept of a linear function arises from the Linear System Theory utilized in Neural Dynamics. Equations like Y=AX and matrix theory use cross-correlations.
It was invented from the Hartline-Ratliff model and the network from the analysis of neural data.
So, now we have understood the different types of RNN. Let us take a simple RNN example to know how it works. Suppose we have to enter the word ‘apple’ and the predictive text function is on. We provide the first three letters ‘a-p-p’, and the network has to predict the rest of the word, and that is ‘l’ and ‘e.’
The vocabulary, in this case, is only four letters. In a real case scenario, you would involve language processing, searching the Wikipedia database of all the words in a language but let’s keep it simple with this small word.
The blue RNN block in a simple RNN structure uses the formula of ht = f(h(t-1), xt)
The first letter of the word apple is ‘a ‘. There is nothing before ‘a’, so we will take the word ‘e.’ After you enter ‘e’ into the system, the above formula is applied, and the previous letter ‘h’ is remembered. After calculating ‘ht,’ the network estimates h(t-1), and it then predicts the final answer ‘apple.’
There are a lot of Recurrent Neural Network applications in the 21st Century. With Artificial Intelligence on the rise, this neural network is one of the popular networks used in machines, mobile phones, and many more.
Its applications include –
In this article, we learned how Recurrent Neural Networks work and how useful they are for us. It can make so many daily tasks easy. With this network’s help, you don’t have to remember words or processes as the machine remembers that for you.
If you’d like to learn more about this topic and other AI-related concepts, check out the Postgraduate Certificate Program In Artificial Intelligence & Deep Learning. This online course is the 8th Best University in NIRF Ranking of 2020. It contains 15 case studies and runs for six months via live online sessions.