Mixed Precision Training in PyTorch and fastai

Deep learning models are growing larger and more computationally expensive, making training speed and memory efficiency critical skills for developers. This text-only course guides you through the mechanics of mixed precision training, showing you how to dramatically speed up your PyTorch and fastai pipelines without sacrificing model accuracy. You will transition from standard single-precision training to modern, memory-efficient workflows that leverage specialized hardware tensor cores. Learn to implement automatic mixed precision, handle numerical stability with gradient scaling, and optimize model throughput. Understand the core concepts of floating-point representations, including FP32, FP16, and modern BF16 formats. Master the integration of mixed precision in both PyTorch native code and high-level fastai wrappers. Practice debugging common numerical underflow issues and stabilizing training runs. Configure your training loops to maximize hardware utilization on modern GPU architectures. We begin with the foundational mathematics of computer arithmetic and floating-point precision before writing any code, ensuring you understand exactly why and how mixed precision works. This course is designed for intermediate programmers and budding machine learning practitioners who have a basic familiarity with neural networks but want to optimize their training workflows. No prior experience with hardware acceleration or low-level optimization is required. Start optimizing your deep learning pipelines today.