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recurrent neural network (RNN)

A Recurrent Neural Network (RNN) is a class of artificial neural networks where connections between nodes form a directed graph along a temporal (time-based) sequence. RNNs belong to the most promising algorithms in use because they are the only type of neural network with an internal memory.

Because of their internal memory, RNNs can remember important things about the input they received, which allows them to be very precise in predicting what’s coming next. This is why they’re the preferred algorithm for sequential data like time series, speech, text, financial data, audio, video, weather and much more. Recurrent neural networks can form a much deeper understanding of a sequence and its context compared to other algorithms.

This allows it to exhibit temporal dynamic behavior and use its internal state (memory) to process sequences of inputs. This differs from traditional feedforward neural networks in that the RNN feedback loop allows the output from a layer to be used as input to the same layer in the next time step of a sequence. RNNs are particularly well-suited for applications where the data has a sequential nature, such as time series prediction, natural language processing, speech recognition, and machine translation[1][2][4].

In more detail, this feedback loop works as follows: when an RNN processes a sequence (e.g., a sentence in natural language processing), each element of the sequence (e.g., each word) is fed into the network one at a time. For each element, the network produces an output and a hidden state. The hidden state is then passed along with the next element of the sequence as input to the network in the next time step. This hidden state acts as the network’s memory, carrying information about what has been processed so far in the sequence. This process allows the RNN to make decisions based on the entire history of inputs it has seen, not just the current input[1].

The ability to pass information across time steps via the hidden state is what enables RNNs to handle sequential data effectively. It allows the network to recognize patterns across time, making it particularly useful for tasks where context and order matter, such as language translation, speech recognition, and time series prediction.

However, managing this memory across long sequences can be challenging due to issues like the vanishing gradient problem, where the network becomes less capable of learning dependencies between distant elements in a sequence. This limitation has led to the development of more advanced RNN architectures, such as Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRU), which introduce mechanisms to better capture long-term dependencies[1].

Citations:

[1] https://en.wikipedia.org/wiki/Recurrent_neural_network

[2] https://www.analyticssteps.com/blogs/recurrent-neural-network-rnn-types-and-applications

[3] https://www.linkedin.com/advice/1/what-some-challenges-limitations-rnns

[4] https://www.ibm.com/topics/recurrent-neural-networks

[5] https://www.simplilearn.com/tutorials/deep-learning-tutorial/rnn

[6] https://iq.opengenus.org/disadvantages-of-rnn/

[7] https://deepai.org/machine-learning-glossary-and-terms/recurrent-neural-network

[8] https://www.almabetter.com/bytes/articles/recurrent-neural-network

[9] https://s3.amazonaws.com/assets.datacamp.com/production/course_21406/slides/chapter2.pdf

[10] https://www.geeksforgeeks.org/introduction-to-recurrent-neural-network/

[11] https://research.aimultiple.com/rnn/