Recurrent neural networks (RNNs) have carved a niche for themselves in the field of neural network technology due to their intrinsic power and robustness. These networks are distinguished from others by their unique characteristic of having an internal memory. First introduced in the 1980s, it was only recently that RNNs’ extensive capabilities were truly recognised.
This surge in usage and popularity is attributed to long short-term memory (LSTM) development, vast data accumulation, and enhanced computer performance. RNNs, with their memory function, can identify critical aspects of received input and predict future outcomes with great precision. This capability sets them as the go-to algorithm for sequential data types like text, audio, video, financial data, and speech.
Applications and Working Mechanism
The usage of RNNs infuses modern daily life, as they fuel software like Google Translate and Siri. To comprehend RNNs, it's essential to understand feed-forward neural networks and sequential data. Sequential data is a chronological dataset with interconnected elements, seen in DNA sequences and financial data.
Feed-forward neural networks transfer information from the input layer to the output layer via internal layers, without retracing any node. These networks lack memory and, consequently, fail in predicting future outcomes. On the contrary, the information within a recurrent neural network structure loops through, considering prior input experiences along with current input data.
Memory in RNNs
In a regular RNN, short-term memory is observed, which extends to long-term memory with an added LSTM layer. The recurrent neural network recalls the information due to its intrinsic memory, creates the output, duplicates it, and feeds it back to the network. Hence, an RNN elaborately synthesizes past and present data. Importantly, RNNs treat current and prior inputs with assigned weights, which are fine-tuned over time through backpropagation and gradient descent.
Contrary to feed-forward networks mapping a single input to an output, RNNs can establish one-to-many, many-to-many, and many-to-one connections.
Variations of RNNs
There are different versions of RNNs, such as:
- Bidirectional RNNs (BRNN): aiming to increase accuracy by incorporating future data, unlike unidirectional RNNs that depend on past inputs for predictions.
- Gated Recurrent Units (GRUs): another variant aiming to resolve short-term memory issues in RNN models, replacing "cell states" with hidden states and limiting to only two gates- update and reset gates.
Long Short-Term Memory (LSTM) networks, a recurrent neural network type, increase network memory capacity making them apt for learning from past experiences occurring after long gaps. These LSTM networks consist of layers or "LSTM units" improving the RNN's ability to remember inputs over a longer period. This attribute owes to the fact that LSTMs store information similar to computer memory, capable of reading, writing, and deleting data from its memory.