rakesh-yadav/PyTorch-RNN-Tutorial
PyTorch tutorial for using RNN and Encoder-Decoder RNN for time series forecasting
This package helps machine learning practitioners who are new to PyTorch understand and apply recurrent neural networks (RNNs) for time series forecasting. It takes a single stream of time-ordered data and predicts future values or sequences, allowing for hyperparameter tuning and model saving. This is for data scientists or analysts with a basic understanding of machine learning concepts but who are less familiar with PyTorch.
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Use this if you need to predict future values of a single, evenly spaced time series and want to learn how to implement RNNs in PyTorch.
Not ideal if you need to forecast using multiple related time series or require a more complex, production-ready deep learning framework.
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41
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Language
Jupyter Notebook
License
Apache-2.0
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Last pushed
Jan 13, 2023
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