Model Training Tips
Training deep learning models is both an art and a science. Here are battle-tested tips to improve your training process and achieve better results.
Data Preparation
1. Normalize Your Inputs
Scale features to similar ranges (e.g., 0-1 or mean=0, std=1)
X = (X - mean) / std2. Data Augmentation
For images: rotation, flipping, cropping. For text: back-translation, synonym replacement.
3. Handle Class Imbalance
Use weighted loss, oversampling, or SMOTE for minority classes.
Training Strategies
Learning Rate Schedule
- Start with 1e-3 or 1e-4
- Use warmup for first few epochs
- Decay with cosine annealing
- Try learning rate finder
Batch Size
- Larger = faster, more stable
- Smaller = better generalization
- Use gradient accumulation if GPU limited
- Typical: 32, 64, 128, 256
Regularization Techniques
Dropout
Randomly drop neurons during training. Typical: 0.2-0.5
Early Stopping
Stop training when validation loss stops improving. Patience: 5-10 epochs.
Weight Decay (L2)
Penalize large weights. Typical: 1e-4 to 1e-2
Debugging Tips
- ✓ Overfit on small batch first - If you can't overfit, model/data has issues
- ✓ Monitor gradients - Check for vanishing/exploding gradients
- ✓ Visualize predictions - Look at what the model is actually predicting
- ✓ Check data pipeline - Ensure labels match inputs correctly
- ✓ Use tensorboard/wandb - Track metrics in real-time
Golden Rule: Start simple, get it working, then add complexity. Don't optimize prematurely!