CS5720 - Week 5
Slide 91 of 100

CNN Training Techniques

🎯
Weight Initialization
Proper weight initialization is crucial for stable training and convergence. Poor initialization can lead to vanishing or exploding gradients.
Key Benefits:
  • Faster convergence
  • Stable gradient flow
  • Better final performance
  • Reduced training instability
🚀
Advanced Optimizers
Modern optimizers like Adam, RMSprop, and AdamW provide adaptive learning rates and momentum for more efficient training.
Key Benefits:
  • Adaptive learning rates
  • Built-in momentum
  • Robust to hyperparameters
  • Faster convergence
🛡️
Regularization
Techniques like dropout, batch normalization, and L2 regularization prevent overfitting and improve generalization.
Key Benefits:
  • Prevents overfitting
  • Better generalization
  • Stable training dynamics
  • Improved robustness
📊
Learning Rate Scheduling
Dynamically adjusting the learning rate during training helps fine-tune the model and achieve better final performance.
Key Benefits:
  • Fine-grained control
  • Better final accuracy
  • Smooth convergence
  • Escape local minima

CNN Training Pipeline

1
Data Preparation
Load, preprocess, and augment training data
2
Model Setup
Initialize architecture and weights
3
Training Loop
Forward pass, loss calculation, backprop
4
Validation
Monitor performance and adjust
5
Deployment
Model optimization and serving

Training Best Practices

📈 Monitor Training Metrics
Track loss, accuracy, learning rate, and gradient norms to identify training issues early.
💾 Save Model Checkpoints
Regular checkpointing prevents loss of progress and enables model recovery from failures.
🎯 Use Validation Sets
Hold out validation data to monitor generalization and prevent overfitting.
🔄 Ensure Reproducibility
Set random seeds and document hyperparameters for reproducible results.
🌊 Gradient Clipping
Prevent exploding gradients by clipping gradient norms to a maximum value.
⏹️ Early Stopping
Stop training when validation performance stops improving to prevent overfitting.
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Prepared by Dr. Gorkem Kar