Long Short-Term Memory (LSTM) is a type of recurrent neural network used in artificial intelligence technology. It helps to solve the vanishing gradient problem that RNN (Recurrent Neural Network) encounters due to the shallow learning model. The vanishing gradient problem occurs when the gradient diminishes too quickly as it passes through multiple layers of a neural network, causing the weights of the first few layers to remain unchanged. LSTM solves this problem by adding extra cells and input/output gates, which allow the gradients to flow through the network without vanishing quickly.
The Basics of LSTM
At its core, LSTM is designed to solve the vanishing gradient problem in RNN. In a standard RNN architecture, input data is passed through each cell of the network, where it is multiplied by weights and passed through an activation function. The output is then passed to the next cell in the sequence. While this approach is effective for processing simple data, it does not perform well over long sequences of data due to the vanishing gradient problem. LSTM overcomes this issue by adding various structures to its cells - the input gate, forget gate, and output gate.
The Components of LSTM
Forget Gate
The forget gate component of LSTM is responsible for monitoring and controlling the flow of information through the cell. The forget gate takes input from the previous cell's output and the current cell's input and applies a sigmoid function to this input. The sigmoid function makes the output a value between 0 and 1, whereby 1 represents the cell retaining all previous information and 0 represents the cell retaining none of the previous information. The forget gate determines which information to erase and which information to preserve from the cell.
Input gate
The input gate of LSTM determines the amount of new information that should be added to the cell. The input gate takes input from the previous cell's output and the current cell's input and applies a sigmoid function to this input. Then, a tanh function is applied, which creates a vector of new candidate values. These candidate values are then passed through to the next cell, where they can be stored or modified
Output Gate
The output gate of LSTM controls the flow of filtered information to the output. The output gate takes input from the previous cell's output and the current cell's input and applies a sigmoid function to it. The sigmoid function is then applied to the new candidate values, which produce the final output of the cell. The output gate enables LSTM to handle both short-term and long-term dependencies in the data.
Applications of LSTM
LSTM has numerous applications, ranging from speech recognition to natural language processing, sentiment analysis, image captioning, and more. It is especially useful in cases where context and long-term patterns are essential. LSTM is suitable for identifying trends and patterns in data by processing sequences with complex dependencies among features. LSTM can also be used for speech transcription, where it can recognize the different words uttered by humans and convert that speech to text. In sentiment analysis, LSTM can analyze natural language text from social media and classify the sentiment of that text as positive, negative or neutral.
Long Short-Term Memory is a type of RNN designed to handle long-term dependencies in a sequence of data. It prevents the vanishing gradient problem by adding extra components such as the forget gate, input gate, and output gate. These mechanisms enable LSTM to learn and process data efficiently by capturing long-term patterns and relationships in a sequence of data. LSTM has numerous applications across various fields and is useful in speech recognition, natural language processing, and many more areas of artificial intelligence.
