RNNs are a sort of neural network that is both powerful and robust, and they are one of the most promising algorithms in use because they are the only ones having an internal memory.
Recurrent neural networks, like many other deep learning techniques, are relatively new. They were first developed in the 1980s, but it wasn’t until recently that we realized their full potential.
RNNs have risen to prominence as a result of increased computer power, vast amounts of data we now have to deal with, and the advent of long short-term memory (LSTM) in the 1990s.
This is why they’re the chosen algorithm for text, speech, financial data, video, audio, and many other types of sequential data. In comparison to other algorithms, recurrent neural networks can acquire a far deeper grasp of a sequence and its context.
You’ll need a working knowledge of “regular” feed-forward neural networks and sequential data to fully comprehend RNNs.
Sequential data is simply ordered data in which related items appear one after the other. Financial data or the DNA sequence are two examples.
Perhaps the most common sort of sequential data is time-series data, which is just a list of data points in chronological order.
The information in a feed-forward neural network only flows in one direction: from the input layer to the output layer, passing through the hidden layers. The data travels in a straight line through the network, never passing through the same node twice.
Feed-forward neural networks have no recollection of the information they receive and are poor predictors of what will happen next. A feed-forward network has no concept of time order because it only analyzes the current input. Except for its training, it has no recollection of what transpired in the past.
The information in recurrent neural network structure cycles via a loop. When it makes a judgment, it takes into account the current input as well as what it has learned from prior inputs.
As a result, there are two inputs to an RNN: the present and the recent past. This is significant because the data sequence provides critical information about what will happen next, which is why an RNN can perform tasks that other algorithms cannot.
It’s worth noting that RNNs apply weights to both the current and prior inputs. In addition, a recurrent neural network will adjust the weights over time via gradient descent and backpropagation (BPTT).
RNNs may map one to many, many to many, and many to one, whereas feed-forward neural networks map one input to one output.
The layers of an RNN, sometimes referred to as an LSTM network, are built using the units of an LSTM.
RNNs can recall inputs for a long time because of LSTMs. This is due to the fact that LSTMs store information in memory similar to that of a computer. The LSTM has the ability to read, write, and delete data from its memory.