How do Recurrent Neural Networks (RNNs) Work
Recurrent neural networks (RNNs) are a type of artificial neural networks for processing sequential data. Furthermore, they enable us to effectively process time-series data like natural language and audio.
Unlike feed forward or fully connected neural networks, which work by processing data in a single pass, RNNs use feedback loops to pass information from one step to another.
To explain how it works, each neuron maintains a state vector, which represents its internal memory. And with each step, we update this state vector taking into account current state and the state of the previous step.
The basic building block of a recurrent neural network is the recurrent cell. This cell contains a set of weights, which we update through each step in the sequence. Therefore, they accumulate a sort of memory through all the steps.
One of the most popular and practically useful type of recurrent cells is Long Short Term Memory (LSTM) cell. The reason being, they are effective in handling long-term dependencies in sequential data. In other words, they have a better memory than vanilla RNNs.
Inner workings of recurrent neural networks
One unique thing about recurrent neural networks is that we don’t train them with an ordinary backpropagation algorithm. With RNNs we have to use backpropagation through time algorithm.
This type of backpropagation involves unrolling the network over time and computing the gradients of the loss function with respect to the weights at each time step. Basically, we take into account weight values at every step in the sequence, since they differ from step to step.
However, this process comes with a major issue, which we call a problem of vanishing gradients in machine learning lingo. In essence, it means that they have limited long-term memory.
This is also why many prefer LSTM cells to vanilla RNN cells, since they address this problem. And quite effectively as well.
In order to overcome this problem, LSTM cells use gating mechanisms, which allow the network to selectively update its internal memory. This in turn makes it easier for the network to learn long-term dependecies.
We can find RNNs in many state-of-the-art systems in a wide range of applications. Some of these are in natural language processing and text classification.
They are also useful in other domains, like speech recognition and time-series forecasting.
Conclusion
In conclusion, Recurrent Neural Networks are a powerful tool for handling sequential data. Furthermore, they have many uses in all sorts of applications.
I hope this article helped you gain a better understanding about RNNs in general and maybe even inspire you to learn even more.
