Feature Request: Add LSTM Algorithm to Neural Network Algorithms

[LEVIII007]

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:

1. 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).

2. 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.

3. Documentation: Provide detailed documentation on the LSTM algorithm's implementation, explaining its structure, hyperparameters, and training process.

4. 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.

[PrakharJain1509]

@LEVIII007 Should I create a separate file (.py) for lstm and also if I resolve this can you update that PR with hactober-accepted? (like if that is possible).

[LEVIII007]

I have created a pull request addressing this issue. You can view it here: Link to PR #12082