CS5720 - Week 3
Slide 59 of 60

Practical Tips for Training Deep Networks

Best Practices & Tips

📊Data Preparation
🏗️Architecture Design
🎯Training Strategy
⚡Optimization Tricks
📈Monitoring & Debugging

Common Problems & Solutions

🔴 Overfitting
Training accuracy high, validation accuracy low. Model memorizes training data.
🟡 Underfitting
Both training and validation accuracy are low. Model is too simple.
🔵 Slow Training
Training takes forever or loss decreases very slowly.
🟣 Vanishing Gradients
Deep layers don't learn. Gradients become extremely small.

Training Checklist - Track Your Progress

🎯 Before Training
  • Data preprocessing and normalization
  • Train/validation/test split
  • Baseline model established
  • Appropriate loss function chosen
  • Metrics defined for evaluation
🏃‍♂️ During Training
  • Monitor loss curves
  • Check validation metrics
  • Save best model checkpoints
  • Watch for overfitting signs
  • Log hyperparameters
🔍 After Training
  • Evaluate on test set
  • Analyze confusion matrix
  • Check for bias in predictions
  • Validate on new data
  • Document results and insights
🚀 Optimization
  • Hyperparameter tuning
  • Data augmentation tried
  • Regularization techniques applied
  • Architecture optimization
  • Ensemble methods considered
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Prepared by Dr. Gorkem Kar