Learn TENSORFLOW with Real Code Examples

Use GPUs or TPUs for faster training

Leverage tf.data pipelines for batch efficiency

Use mixed precision for large models

Profile training to find bottlenecks

Optimize model architecture for inference speed

Sanitize inputs for deployed models

Secure APIs serving predictions

Version models to prevent misuse

Monitor access to sensitive datasets

Use encrypted storage for trained models

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