Best Practices for Effective Training
Master proven techniques to improve convergence, avoid overfitting, and maximize model performance
Data Preparation
Clean, preprocess, and augment data to ensure high-quality training inputs
Learn More →Advanced Techniques
Implement transfer learning, quantization aware training, and mixed precision
Learn More →Training Strategy Frameworks
Data Augmentation
Transform your dataset using rotation, normalization, and synthetic generation to enhance model robustness
tf.image.random_flip_left_right(image)
Best Practices
- • Balance augmentation across classes
- • Use domain-specific transformations
- • Combine with cross-validation
Hyperparameter Tuning
Learning Rate
Use cyclic learning rates or adaptive optimizers (AdamW)
Batch Size
Find optimal balance between accuracy and training speed
Regularization
Implement dropout, weight decay, and early stopping
Use automated search with Ray Tune or Optuna
Distributed Training
When to Use
- • Large datasets (>1TB)
- • Deep architectures (Transformer models)
- • Time-sensitive training
Implementation
Implement with frameworks like:
Recommended Training Tools
Weights & Biases
Experiment tracking for hyperparameter management and model performance metrics
MLflow
Model registry and deployment pipelines integration
PyTorch Lightning
Simple abstractions for complex distributed training scenarios