Recurrent Neural Networks (RNNs) for Sequence Data

RNNs (Recurrent Neural Networks) are designed for sequential data — time series, text, audio, or sensor logs. Unlike regular neural networks, RNNs have a memory: they process inputs one step at a time and pass a "hidden state" from step to step. LSTMs (Long Short-Term Memory) are an advanced version that can remember information over long sequences, solving the "forgetting" problem of basic RNNs.

• RNN/LSTM input shape: (batch_size, timesteps, features) — e.g., 30 days of 3 sensors.
• LSTMs are used for weather forecasting, stock prediction, text generation, and speech recognition.
• In South Sudan, LSTM models could predict river levels from historical sensor data for early flood warnings.
• Use TensorFlow/Keras LSTM layer — simple to add to any model.

💡 Practical Skill: Process time-series data from IoT sensors or weather stations. Clean the data in Jupyter Notebooks, structure it as sequences, and train an LSTM to make predictions.

# Setting up an LSTM model for time-series prediction# Input: 10 timesteps, each with 1 feature (e.g., daily temperature) import tensorflow as tf # Define the LSTM modelmodel = tf.keras.Sequential([    # LSTM layer: 50 memory units, expecting sequences of 10 steps    # input_shape = (timesteps, features)    tf.keras.layers.LSTM(50, activation='tanh', input_shape=(10, 1)),     # Dense output layer: predict the next value    tf.keras.layers.Dense(1)]) # Compile the modelmodel.compile(optimizer='adam', loss='mse') print("\u2705 LSTM model ready for sequence data!")print(f"   Input shape: (batch_size, 10 timesteps, 1 feature)")print(f"   Output: 1 value (next step prediction)") # Show model architecturemodel.summary() # Example: simulate how you'd reshape raw sensor data# Raw data: [25, 26, 27, 26, 25, 24, 23, 24, 25, 26, 27, 28]# Sequences of 10 steps => predict the 11th value print("\nData shape hint: reshape raw sensor logs to")print("   (num_sequences, 10, 1) before training.")