Learn KERAS with Real Code Examples

Use GPU acceleration

Batch data efficiently

Use mixed precision for large models

Leverage TensorFlow dataset pipelines

Profile training to identify bottlenecks

Sanitize input data in production

Avoid exposing raw models to untrusted sources

Secure APIs serving predictions

Validate external datasets

Use model versioning to prevent misuse

TensorBoard metrics visualization

Training/validation loss and accuracy

GPU/CPU profiling

Logging callbacks

Experiment tracking with MLFlow or Weights & Biases

Organize modular layer and model code

Document architecture and parameters

Use callbacks for reproducibility

Profile training for performance

Follow Python coding standards