Part 1: Hyperparameter Calibration with Weights & Biases

• Implement hyperparameter tuning using Weights & Biases (wandb)
• Choose one of the following problems to apply this to:
  • TF binding prediction
  • Simple DNA scoring
  • Cubic function with MLP
• Reference example: TF Binding with wandb
• Document the hyperparameters you tuned and their impact on model performance

Part 2: Model Deployment and Testing

• Upload your complete Jupyter notebook containing:
  • Model architecture
  • Training code
  • Evaluation metrics
  • Prediction code
• Include a separate, self-contained prediction script that:
  • Loads all required packages
  • Loads the trained model architecture and weights
  • Makes predictions on the hold-out test set
  • Can be run independently without executing the entire notebook

Submission Requirements

1. Submit your Jupyter notebook with all code and documentation
2. Include a separate prediction script as described above
3. Document your hyperparameter tuning process and results
4. Provide clear instructions for running the prediction script

Evaluation Criteria

• Proper implementation of hyperparameter tuning
• Model performance on test set
• Code organization and documentation
• Reproducibility of results