Recurrent Neural Networks (RNNs) are powerful tools for processing sequential data like text, time series, or speech. This tutorial demonstrates how to implement RNNs using TensorFlow with practical examples.

Key Concepts of RNNs

  • Sequence Processing: RNNs maintain memory of previous inputs via hidden states (🡺 h_t = f(x_t, h_{t-1}))
  • Vanishing Gradient Problem: Traditional RNNs struggle with long-term dependencies (🔥 Read more)
  • Variants: LSTM and GRU are popular improvements for sequence modeling

TensorFlow RNN Example

import tensorflow as tf

# Sample data: sequence of numbers
data = tf.constant([[1, 2, 3], [4, 5, 6]], dtype=tf.float32)
labels = tf.constant([[3, 4], [6, 7]], dtype=tf.float32)

# Simple RNN model
model = tf.keras.Sequential([
    tf.keras.layers.SimpleRNN(16, return_sequences=True),
    tf.keras.layers.Dense(1)
])

model.compile(optimizer='adam', loss='mse')
model.fit(data, labels, epochs=10)

Applications of RNNs

  • Time series prediction 📈
  • Language modeling 📖
  • Sentiment analysis 😊/😢
  • Music generation 🎵

Visual Aids

RNN Structure
*Figure: Basic architecture of a Recurrent Neural Network*

Further Reading

For advanced RNN techniques, check out our LSTM tutorial to understand how to overcome the vanishing gradient problem.