Neural Turing Machines (NTMs) are a kind of recurrent neural network (RNN) that utilizes external memory resources. This repository contains a PyTorch implementation of NTMs.
This project is an implementation of the Neural Turing Machine, as proposed in the paper "Neural Turing Machines" by Graves, Wayne, and Danihelka. The purpose of this project is to create a machine learning model that can learn to infer and execute simple algorithms, extending beyond the capabilities of typical sequence-to-sequence models.
Prerequisites:
- Python 3.x
- PyTorch 1.x
- Numpy
Installation Steps:
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Clone the repository: git clone https://github.com//Neural-Turing-Machines.git
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Install the necessary packages: pip install -r requirements.txt
This section is currently under development as we refine the interfaces and usage patterns for our project. Please note that as we are actively developing these, they may change.
This project is currently under active development. Features may be added, removed, or changed, and there may be bugs.
If you have any questions, comments, or would like to contribute to this project, feel free to reach out to me.
Email: [email protected]
This project is based on the ideas presented in the following paper:
Graves, A., Wayne, G., & Danihelka, I. (2014). Neural Turing Machines. arXiv preprint arXiv:1410.5401. Link to the paper