Once You Get Norm Placement Correct, Your Training Speed Changes IMMEDIATELY. (This is how) скачать в хорошем качестве

Once You Get Norm Placement Correct, Your Training Speed Changes IMMEDIATELY. (This is how)

Most training instability doesn’t come from bad data or weak GPUs — it comes from one misplaced line of code. In this video, we break down one of the most overlooked design choices in deep learning: where you place your normalization layers. Using a simple Transformer-style block, we compare Post-Norm (the original approach) with Pre-Norm (the modern standard used in GPT-4 and LLaMA) and show why the difference matters. You’ll see how Post-Norm quietly leads to exploding gradients and NaNs as models get deeper, and why Pre-Norm keeps gradients healthy, training stable, and learning faster — without changing model size or data. If you’ve ever had a model that should train but doesn’t, this is likely the reason. One line. Massive impact.