Building a Hybrid CNN-LSTM Model with PyTorch & Attention (Part 3)

In Part 3 of this series, learn how to build a Hybrid CNN-LSTM model with PyTorch and Attention for Structural Health Monitoring. We cover:

1. Implementing CNN + LSTM for time-series data
2. Adding a custom Attention mechanism
3. Handling tensor reshaping & forward pass
4. Final classification layer setup
5. Perfect for deep learning practitioners working with time-series problems.

Videos recommended to watch first before watching this one :

Data Cleaning :    • Bridge Sensor Data : Initial Inspection an...  
Time Based Feature Engineering :    • Transforming Time-Series Data: From Raw Si...  

🕒 Timestamps

0:00 – Introduction & Problem Justification → Structural health monitoring & weak feature correlations
1:00 – Model Implementation (models.py) & Reproducibility → Setting seeds for consistent results
3:30 – Custom Attention Module → Weighing importance of timesteps
5:15 – Hybrid Model Architecture (CNN + LSTM) → Combining local & long-term features
6:30 – Forward Pass & Data Flow → Tensor permutation & reshaping explained
7:00 – Attention Mechanism & Final Classification → Applying attention + fully connected layer
8:05 – Conclusion & Next Steps → Preparing for training script in Part 4

Model Architecture python script here :
https://github.com/arosha27/Structura...

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#education

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