https://github.com/aminkhavari78/text-generation-with-lstm-recurrent-neural-networks-in-python-with-keras
use LSTM model for text generation
https://github.com/aminkhavari78/text-generation-with-lstm-recurrent-neural-networks-in-python-with-keras
dropout io lstm-neural-networks numpy pandas sequence-models sys text-processing
Last synced: 3 months ago
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use LSTM model for text generation
- Host: GitHub
- URL: https://github.com/aminkhavari78/text-generation-with-lstm-recurrent-neural-networks-in-python-with-keras
- Owner: AminKhavari78
- License: mit
- Created: 2022-10-17T10:22:49.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2022-10-17T10:33:32.000Z (almost 3 years ago)
- Last Synced: 2025-06-04T03:40:14.132Z (4 months ago)
- Topics: dropout, io, lstm-neural-networks, numpy, pandas, sequence-models, sys, text-processing
- Language: Jupyter Notebook
- Homepage:
- Size: 8.79 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Text-Generation-With-LSTM-Recurrent-Neural-Networks-in-Python-with-Keras
Recurrent neural networks can also be used as generative models.
This means that in addition to being used for predictive models (making predictions), they can learn the sequences of a problem and then generate entirely new plausible sequences for the problem domain.
Generative models like this are useful not only to study how well a model has learned a problem but also to learn more about the problem domain itself.