Recurrent Neural Networks (RNNs) are a class of artificial neural networks designed to recognize patterns in sequences of data, such as time series or natural language. They are widely used in tasks like language modeling, speech recognition, and sequence prediction. Below are key concepts and examples related to RNNs in TensorFlow.

Key Features of RNNs

  • Temporal Dynamics: Process sequential data by maintaining a hidden state that captures information about previous elements in the sequence.
  • Unrolling: Visualize the network as a series of connected layers for time steps.
  • Vanishing Gradient Problem: A challenge in training long sequences, mitigated by variants like LSTM and GRU.

TensorFlow RNN Tutorials

  1. Basic RNN Implementation
    🔗 View tutorial

    Recurrent_Neural_Network

  2. LSTM and GRU Networks
    🔗 Advanced guide

    LSTM_GRU_Network

  3. Sequence-to-Sequence Models
    🔗 Practical example

    Sequence_to_Sequence_Model

Applications

  • Natural Language Processing (NLP): Text generation, sentiment analysis.
  • Speech Recognition: Converting audio signals into text.
  • Time Series Forecasting: Predicting future values based on historical data.

For deeper understanding, explore TensorFlow's official RNN documentation. 📚