LSTM (Long Short-Term Memory) networks are a type of recurrent neural network (RNN) that is capable of learning long-term dependencies. They are particularly useful for time-series prediction tasks.

Key Features

  • Recurrent Structure: LSTM networks have a recurrent structure, which allows them to remember information over time.
  • Forget Gate: The forget gate helps the network to forget irrelevant information.
  • Input Gate: The input gate determines which new information to store in the memory.
  • Output Gate: The output gate controls what information to output based on the current state of the memory.

Applications

  • Stock Price Prediction: LSTM networks can be used to predict future stock prices based on historical data.
  • Weather Forecasting: They can forecast weather conditions based on historical weather data.
  • Speech Recognition: LSTM networks are used in speech recognition systems to convert spoken words into text.

Implementation

Here's a simple example of how to implement an LSTM network using TensorFlow:

import tensorflow as tf

model = tf.keras.Sequential([
    tf.keras.layers.LSTM(50, return_sequences=True),
    tf.keras.layers.LSTM(50),
    tf.keras.layers.Dense(1)
])

model.compile(optimizer='adam', loss='mean_squared_error')

Further Reading

For more information on LSTM networks, you can visit the following resources:

LSTM Network