Mixed Precision Training | Explanation and PyTorch Implementation from Scratch

In this video, we break down Mixed Precision Training. You’ll learn why FP16, BF16, and FP32 matter, what we gain (and lose) when we switch precision, and how mixed precision training lets us train AI models faster and with lesser resources without sacrificing accuracy. We start by understanding floating point formats(specifically FP32), what precision is , and from there transition to lower precision formats like FP16, BF16 . We then explore the real benefits of lower precision, implement mixed precision from scratch, and finally switch to PyTorch’s built-in AMP for training our deep learning models. Training deep neural networks keeps getting more expensive as models grow larger and more complex. Even with powerful GPUs, the compute demand increases almost every year, and hence we need to make deep learning training as efficient as we can, mixed precision training is one such technique that allows us to train large ai models in half the resources. ⏱️ Timestamps 00:00 Why care about Mixed Precision ? 01:19 What is Precision? (FP32 vs FP16 vs BF16 Explained) 10:55 Why Lower Precision Helps 14:01 Mixed Precision Training From Scratch (Step-by-Step) 25:10 Loss Scaling 29:30 Mixed Precision Training in PyTorch (autocast + GradScaler) 31:50 Summary 📖 Resources Mixed Precision Training Paper : https://arxiv.org/pdf/1710.03740 Nice video covering more details on floating point representations : https://www.youtube.com/watch?v=bbkcEiUjehk Video to understand more on denormalized numbers - https://www.youtube.com/watch?v=aPsSAEmwhgA 🔔 Subscribe : https://tinyurl.com/exai-channel-link Email - explainingai.official@gmail.com