Deep RNNs and Difference in Parameters

This repository contains a notebook exploring deep Recurrent Neural Network (RNN) architectures and their parameter differences, implemented using TensorFlow/Keras.

Overview

The notebook investigates:

• How stacking multiple RNN layers (deep RNNs) compares to using wider single-layer RNNs.
• Differences in the number of trainable parameters across various RNN architectures.
• Practical insights into designing RNNs for sequential data using TensorFlow.
• Examples with both SimpleRNN and LSTM layers.

Notebook Details

The notebook deep_rnns_and_parameteric_difference.ipynb includes:

• Building RNN and LSTM models with varying layers and hidden units using TensorFlow Keras.
• Calculating and comparing trainable parameters for different configurations.
• Analyzing the trade-offs between network depth and width.
• Visual demonstrations of parameter count differences.

Usage

This notebook is ready to run on Google Colab:

1. Open Google Colab.
2. Upload or open the notebook file deep_rnns_and_parameteric_difference.ipynb.
3. Run the notebook cells to explore deep RNN architectures and their parameter differences.

Requirements

• TensorFlow is pre-installed in Google Colab, so no additional setup is needed.

Author

Haseeb Ul Hassan

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Feel free to explore and modify the notebook to deepen your understanding of deep RNN parameterization with TensorFlow!