This skill introduces the use of mixed precision (FP16 + FP32) to accelerate training while maintaining model accuracy. It focuses on numerical stability, hardware support, and integration with modern ML frameworks.

• External: Basic understanding of floating point computation
• Internal: BDA5.3.1 Pytorch or BDA5.3.2 Tensorflow (recommended)

• Define mixed precision training and describe its benefits in performance and memory usage.
• Identify hardware and software prerequisites for mixed precision support (e.g., NVIDIA Tensor Cores, AMP).
• Apply automatic mixed precision (AMP) in PyTorch and TensorFlow workflows.
• Monitor for numerical instability and apply scaling techniques as needed.
• Benchmark training speed and accuracy trade-offs using mixed precision.

Caution: All text is AI generated