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Recurrent neural network


Recurrent neural networks (RNNs) are a type of artificial neural network (ANN) that is well-suited for sequential data, such as natural language, speech, and time-series data.

Unlike feedforward neural networks, which process each input independently, RNNs have a feedback loop that allows them to remember information from previous inputs.

Architecture of RNNs

The basic architecture of an RNN consists of a series of hidden layers, each of which contains a number of recurrent units.

These recurrent units are the building blocks of RNNs and are responsible for processing sequential data.

Each recurrent unit receives input from the previous unit in the sequence, as well as from the external input.

It then processes this information and produces an output, which is passed on to the next unit in the sequence.

Types of RNNs

1. Simple RNNs: Simple RNNs are the most basic type of RNN. They have a single hidden layer and a simple feedback loop. Simple RNNs are prone to the vanishing gradient problem, which makes it difficult to train them on long sequences of data.

2. Long short-term memory (LSTM) networks: LSTM networks are a type of RNN that is designed to overcome the vanishing gradient problem. They have a special type of recurrent unit called an LSTM cell, which has a more complex feedback loop that allows it to learn long-term dependencies in data.

3. Gated recurrent unit (GRU) networks: GRU networks are another type of RNN that is designed to overcome the vanishing gradient problem. They have a simpler feedback loop than LSTM networks, but they are still able to learn long-term dependencies in data.

Training Process of RNNs

The training process of an RNN is similar to the training process of other types of neural networks.

The goal of training is to find a set of weights for the connections between the recurrent units that minimize a loss function.

The loss function measures the difference between the network’s predictions and the true labels.

There are many different algorithms for training RNNs, but some of the most common include:

1. Backpropagation through time (BPTT): BPTT is a variant of backpropagation that is specifically designed for RNNs. It unrolls the RNN over time and then applies backpropagation to the unrolled network.

2. Real-time recurrent learning (RTRL): RTRL is another variant of backpropagation that is designed for RNNs. It avoids the need to unroll the network by using a technique called dynamic programming.

Applications of RNNs

  • Natural language processing (NLP): RNNs are used for tasks such as machine translation, text summarization, and sentiment analysis.
  • Speech recognition: RNNs are used to convert spoken language into text.
  • Music generation: RNNs are used to generate new music that is similar to existing music.
  • Time-series forecasting: RNNs are used to forecast future values of a time series, such as stock prices or sales figures.

Limitations of RNNs

  • Vanishing gradient problem: The vanishing gradient problem can make it difficult to train RNNs on long sequences of data.
  • Exploding gradient problem: The exploding gradient problem can also make it difficult to train RNNs.
  • Computational complexity: RNNs can be computationally expensive to train.


  • “Recurrent Neural Networks for Natural Language Processing” by Christopher Manning and Hinrich Schütze
  • “Sequence Modeling with RNNs: A Practical Guide” by Janusz Chorowski

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