Welcome to the LSTM tutorial using TensorFlow! This guide will walk you through building a Long Short-Term Memory (LSTM) network for sequence prediction tasks. LSTM is a type of recurrent neural network (RNN) that excels at capturing temporal dependencies in data.

🚀 What is LSTM?

LSTM uses memory cells and gates (input, forget, output) to selectively retain or discard information. This makes it ideal for tasks like:

  • Time series forecasting
  • Sentiment analysis
  • Language modeling
  • Sequence-to-sequence tasks

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🧩 Step-by-Step Implementation

  1. Import Libraries 

    import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense

  2. Prepare Data
    Use time-series datasets like stock prices or sensor data. Example: 

    # Sample data: [x, y] pairs
data = [[0.1, 0.2, 0.3], [0.2, 0.3, 0.4], ...]

  3. Build Model 

    model = Sequential([
    LSTM(50, input_shape=(None, 1)),
    Dense(1)
])
model.compile(optimizer='adam', loss='mse')

  4. Train Model 

    model.fit(X_train, y_train, epochs=20, batch_size=32)

⚠️ Key Considerations

  • Use tf.keras for simplicity
  • Adjust units parameter based on complexity
  • Monitor training with model.summary()
  • For advanced architectures, check our Sequence-to-Sequence guide

📚 Expand Your Knowledge

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