Recurrent Neural Networks (RNNs)

In this lab, we expand our practical skills in deep learning by working with Recurrent Neural Networks (RNNs).
The main goal is to perform time series prediction and classification tasks using different RNN architectures.

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Models Covered

• Simple RNN
• Long Short-Term Memory (LSTM)
• Gated Recurrent Unit (GRU)

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Objectives

• Understand how RNNs handle sequential data
• Implement and train RNN-based models in Python
• Compare model performance for prediction and classification tasks

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Technologies Used

• Python
• TensorFlow / Keras
• NumPy, Pandas, Matplotlib

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Author

Negin Ebrahimi

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This repository is part of the Deep Learning course .