- Label propagation
- Language identification
- Language modeling
- Language translation
- Large margin classifiers
- Latent Dirichlet allocation
- Latent semantic analysis
- Layer-wise relevance propagation
- Learning from imbalanced datasets
- Learning from noisy data
- Learning to rank
- Lexical analysis
- Linear algebra
- Linear discriminant analysis
- Linear dynamical systems
- Linear programming
- Linear regression
- Linear-quadratic-Gaussian contro
- Link analysis
- Link prediction
- Local binary patterns
- Local feature extraction
- Locality-sensitive hashing
- Logical inference
- Logical reasoning
- Logistic regression
- Long short-term memory networks
- Low-rank matrix completion
- Low-rank matrix factorization
What is Long short-term memory networks
The Magic of Long Short Term Memory Networks (LSTM)
Long Short-Term Memory Networks (LSTM) is a kind of Recurrent Neural Network (RNN) architecture that is capable of selectively remembering past inputs and using the stored information to make predictions. LSTMs have managed to outperform other state-of-the-art models in various fields of study including speech recognition, machine translation, natural language processing, and more. Let us delve deeper into this fascinating topic and understand the inner workings of LSTM better.
The Problems with Traditional RNNs
Recurrent Neural Networks (RNN) work by processing sequential data like time-series data, speech, and language. The main drawback of RNN is the vanishing gradient problem. This problem occurs when the gradients propagate through the network, and the gradient in the earlier layer dims away till it becomes practically zero, thereby ending the learning process. The problem becomes even more pronounced when dealing with long sequences as the error signal weakens over time. As a result, the network fails to connect the effect of the earlier inputs to the current predictions accurately.
Addressing the Vanishing Gradient Problem with LSTMs
LSTM is an RNN architecture that addresses the vanishing gradient problems of traditional RNNs. The key to LSTMs being able to avoid the vanishing gradient problem is the use of gated cells responsible for storing or clearing the cell state. The gates facilitate the manipulation of the information flow, allowing the network to selectively remember or forget information. The three gates used by LSTMs are:
- The Input Gate: The input gate determines the amount of information that is to be stored in a memory cell. The input is propagated via a sigmoid function that determines the fraction of the candidate vector to be added to the memory cell.
- The Forget Gate: The forget gate determines the amount of information to be discarded from the cell memory. This is achieved through a sigmoid function that determines the fraction of information to be kept or discarded from the memory cell.
- The Output Gate: The output gate decides the amount of output to push to the next layer, given the current state of the cell. The current cell input and the previous cell state are combined and passed through a sigmoid function to generate an output.
The Anatomy of an LSTM Cell
LSTM consists of one or more lstm cells that work together to allow the network to selectively learn, remember and forget over long sequences of sequential data. Each cell consists of a memory cell and three gates.
The role of the three gates is as follows:
- The Input Gate: The input gate is the function that determines how much information should be passed to the memory cell. The input is passed through a sigmoid function, which produces a value between 0 and 1. This value is multiplied by the candidate vector, which contains the proposed information to be stored. The result gives us the amount of information to be added to the memory cell from the candidate vector.
- The Forget Gate: The forget gate is responsible for regulating the amount of data that should be retained in the cell. It works by using the sigmoid function to convert the input data and the previous cell state into a probability between 0 and 1 that should be multiplied with the previous cell state. The result gives us the amount of information that should be retained in the cell.
- The Output Gate: The output gate is responsible for outputting data from the cell. It calculates the value of the cell state and passes it through a sigmoid function to obtain a value between 0 and 1. This value is multiplied by the cell state to get the output value.
LSTM consists of one or more LSTM networks, and each network has an input layer, an output layer, and one or more hidden layers. The input layer processes sequential data input, and each cell output goes to the output layer that produces the final prediction.
Applications of LSTM
LSTMs have significant applications in various fields, and some of them are:
- Speech Recognition: LSTMs can be used to convert speech sound waves into text in an auditory model called Automatic Speech Recognition (ASR). The network can achieve better accuracy than the traditional Hidden Markov Model-Based speech recognition systems.
- Sentiment Analysis: LSTMs can be used to predict, classify and track emotions across social media sentiment data. The network model is trained on labeled sets of text examples, such as social media sentiment or product reviews.
- Time Series Prediction: LSTMs have been successfully used to forecast the prices of stocks, commodities, and other financial instruments based on their past prices and other financial data.
- Machine Translation: LSTMs are widely used in machine translation as an alternative to the traditional language-based models. The network models can learn the mapping of text between different languages, given a set of training examples.
Conclusion
Long Short-Term Memory Networks (LSTM) provides a powerful tool in our quest to learn from sequential data and make predictions. LSTMs have the power to address the vanishing gradient problem of traditional RNNs by selectively remembering and forgetting past information, making them the ideal choice for sequential applications. The growing demand for Deep learning models in various fields of science, technology, and business is a promising sign that LSTM will continue to evolve and become even more powerful in the coming years.