(RNN) Recurrent neural network

PUBLISHED: MAY 2, 2026•2 MIN READ

Recurrent Neural Network (RNN)A Recurrent Neural Network (RNN) is a type of neural network designed to handle sequential data. Unlike traditional feed-forward n

Abhishek Singh Rajput
Abhishek SinghAuthor
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Recurrent Neural Network (RNN)

A Recurrent Neural Network (RNN) is a type of neural network designed to handle sequential data. Unlike traditional feed-forward networks, RNNs use connections that form cycles, allowing them to maintain a "memory" of previous inputs. This makes them suitable for tasks where the current output depends on previous steps (e.g., predicting the next word in a sentence).

How RNN Works

What is Recurrent Neural Networks (RNN)?

  • Input vector ​ is passed into the hidden layer at each time step .
  • The hidden state ​ is calculated using both the current input and the previous hidden state ​.
  • The output ​ is generated from the hidden state.
  • At the final time step, the last hidden state can be used to calculate the overall output.
  • Errors are backpropagated through time (BPTT – Backpropagation Through Time) to update the weights.

Why RNN is Needed

  • Feed-forward networks cannot handle sequential data as they only consider the current input.
  • They cannot memorize past inputs/outputs.
  • RNNs solve this by retaining information through hidden states, making them effective for sequential tasks like speech, text, and time-series prediction.

Types of RNN

Types of RNN (Recurrent Neural Network)

  1. One-to-One → Simple input → output mapping (e.g., image classification).
  2. One-to-Many → Single input, multiple outputs (e.g., image captioning).
  3. Many-to-One → Multiple inputs, single output (e.g., sentiment analysis).
  4. Many-to-Many → Multiple inputs and outputs (e.g., machine translation, video processing).

Advantages of RNN

  • Sequential memory: Retains information from previous inputs.
  • Time-series prediction: Past data helps predict future values.
  • Combination with CNNs: Can be used with convolutional layers to capture spatial + sequential features (useful in video and image tasks).

Limitations of RNN

  • Vanishing gradient problem: Gradients shrink during backpropagation, making it hard to learn long-term dependencies.
  • Exploding gradient problem: Gradients grow too large, causing unstable training.
  • Slow training: Sequential nature limits parallelization compared to CNNs/Transformers.

Applications of RNN

  • Speech recognition
  • Time-series prediction
  • Natural Language Processing (NLP):
    • Language modeling
    • Sentiment analysis
    • Machine translation
  • Image & video processing