Feature Request: Add LSTM Algorithm to Neural Network Algorithms #12069
Description
Feature description
Add LSTM Algorithm to Neural Network Algorithms
Feature Description:
I would like to propose adding an LSTM (Long Short-Term Memory) algorithm to the existing neural network algorithms in the repository. LSTMs are a type of recurrent neural network (RNN) that excel in handling sequential and time-series data, making them particularly valuable for tasks such as language modeling, text generation, and time-series forecasting.
Proposed Improvements:
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Implementation of LSTM: Develop a comprehensive LSTM class that includes essential functionalities such as:
- Forward propagation through LSTM layers.
- Backpropagation through time (BPTT) for training.
- Methods for saving and loading the model.
- Support for various activation functions (sigmoid, tanh, softmax).
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Example Usage: Include example usage code demonstrating how to train the LSTM on a dataset, such as predicting the next character in Shakespeare's text.
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Documentation: Provide detailed documentation on the LSTM algorithm's implementation, explaining its structure, hyperparameters, and training process.
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Unit Tests: Implement unit tests to ensure the correctness and robustness of the LSTM functionality.
Rationale:
Adding LSTM capabilities will enhance the versatility of the neural network algorithms available in this repository, allowing users to tackle a wider range of problems involving sequential data. Given the growing importance of time-series analysis and natural language processing, this addition would significantly benefit the community.