Recurrent Neural Networks (RNNs) for Advanced Predictions

Glossary/R/Recurrent Neural Networks (RNNs)

What are Recurrent Neural Networks (RNNs)?

Recurrent Neural Networks (RNNs) are a type of neural network commonly used for sequential data processing tasks, such as natural language processing, speech recognition, and time series analysis. RNNs have the ability to retain information from previous steps and use it to make predictions or generate sequences.

Unlike feedforward neural networks, where information flows in one direction from input to output, RNNs introduce feedback connections that allow information to persist. This feedback connection creates a loop, enabling the network to store and process information about the sequence it has seen so far.

Related terms

Neural Network

A neural network is a fundamental concept in the field of artificial intelligence and machine learni...

Natural Language Processing (NLP)

Natural Language Processing (NLP) is a branch of artificial intelligence (AI) that focuses on enabli...

Not to be confused with:

Neural Network

A neural network is a fundamental concept in the field of artificial intelligence and machine learni...

Natural Language Processing (NLP)

Natural Language Processing (NLP) is a branch of artificial intelligence (AI) that focuses on enabli...

Neural Network

A neural network is a fundamental concept in the field of artificial intelligence and machine learni...

Natural Language Processing (NLP)

Natural Language Processing (NLP) is a branch of artificial intelligence (AI) that focuses on enabli...

Neural Network

A neural network is a fundamental concept in the field of artificial intelligence and machine learni...

Natural Language Processing (NLP)

Natural Language Processing (NLP) is a branch of artificial intelligence (AI) that focuses on enabli...

The key feature of RNNs is their ability to handle variable-length input sequences. At each step of the sequence, the RNN takes an input and updates its internal state based on the current input and the information it has learned from previous steps. The internal state, also known as the hidden state, serves as a memory that captures the context and dependencies between the elements in the sequence.

RNNs have been successful in various applications. For example, in natural language processing, RNNs can understand the context of words and generate coherent sentences. In speech recognition, RNNs can transcribe spoken words into text. In time series analysis, RNNs can predict future values based on past observations.

RNNs can also be combined with other architectures for more powerful models. For instance, the attention mechanism can be integrated with RNNs, enabling the network to focus on relevant parts of the input sequence and improve performance in tasks such as machine translation or sentiment analysis.

Back to glossary