Neural Turing Machine in TensorFlow
TensorFlow implementation of Neural Turing Machines with LSTM controllers, supporting multiple read/write heads.
What is Neural Turing Machine in TensorFlow?
NTM-tensorflow is a TensorFlow implementation of Neural Turing Machines, a neural network architecture that integrates external memory with neural controllers to learn algorithms and store information. It solves the problem of enabling neural networks to perform complex memory-based tasks like copying sequences, advancing research in machine learning and artificial intelligence.
Machine learning researchers and developers interested in memory-augmented neural networks, sequence learning, or exploring TensorFlow implementations of advanced neural architectures.
Developers choose this project for its open-source TensorFlow implementation of NTMs with support for multiple read/write heads, providing a practical reference for experimenting with memory-augmented models, despite noted limitations in handling variable-length inputs.
Overview
"Neural Turing Machine" in Tensorflow
Use Cases
Best For
- Implementing Neural Turing Machines in TensorFlow for academic research
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