What LSTM (Long Short-Term Memory) Meaning, Applications & Example
A type of recurrent neural network that can learn long-term dependencies.
What is LSTM (Long Short-Term Memory)?
LSTM (Long Short-Term Memory) is a type of recurrent neural network (RNN) designed to model sequential data and overcome the limitations of traditional RNNs, such as the vanishing gradient problem. LSTMs are capable of learning long-term dependencies in data by using a special architecture that includes memory cells to store information over time.
How LSTM Works
- Memory Cells: Each cell in an LSTM stores information over time, allowing the network to remember and forget specific pieces of data as needed.
- Gates: LSTMs use three types of gates:
- Forget Gate: Decides which information from the previous time step should be discarded.
- Input Gate: Determines what new information should be added to the memory.
- Output Gate: Controls which information is output based on the current state and memory.
- Backpropagation Through Time: LSTM models use a modified version of backpropagation to update the weights of the network, allowing it to learn long-term dependencies.
Applications of LSTM
- Time Series Forecasting: Used to predict future values based on past data, such as stock prices or weather conditions.
- Natural Language Processing (NLP) : Employed in tasks like language modeling, machine translation, and text generation.
- Speech Recognition: Helps convert audio signals into text by learning temporal patterns in speech data.
- Video Analysis: Analyzes sequences of frames in videos for object tracking or activity recognition.
Example of LSTM
In text generation, LSTMs are often used to predict the next word in a sentence based on the previous words. By learning from large datasets of text, the LSTM model captures the patterns of language, enabling it to generate coherent text one word at a time, often used in chatbots or automated content generation.