📌 1. Data Augmentation Strategies

Enhance model generalization by applying transformations like:

  • 🖼️ Rotation (e.g., Rotation_90_degrees)
  • 🖼️ Flipping (e.g., Flip_horizontal)
  • 🖼️ Cropping (e.g., Cropping_techniques)

Tip: Use Data_augmentation_techniques to visualize how augmentations expand the training dataset.

📌 2. Transfer Learning for CNNs

Leverage pre-trained models (e.g., VGG, ResNet) and fine-tune them for your task:

  • 🔄 Freeze base layers to retain learned features
  • 🔄 Unfreeze specific layers for domain adaptation
  • 🔄 **Use transfer learning_illustration` to see architecture flow.

📌 3. Model Optimization Techniques

Improve performance with:

  • ⚙️ Batch Normalization (e.g., Batch_normalization)
  • ⚙️ Dropout regularization (e.g., Dropout_regularization)
  • ⚙️ **Learning_rate_scheduling` for adaptive training.

📚 4. Further Reading

Explore related topics:

Advanced_CNN