Randomness & Reproducibility in PyTorch |Why Deep Learning Models Behave Differently Every Run |2025

Описание: Ever trained the same PyTorch model twice and got different results - even with identical code?
That’s not a bug, it’s randomness - and it’s everywhere in deep learning. From weight initialization and data shuffling to dropout layers, PyTorch introduces randomness that helps models generalize, but it also makes debugging and comparing experiments a nightmare.

In this video, we’ll break down:
Why randomness exists in neural networks
How it affects your model’s behavior and accuracy
The trade-off between generalization and reproducibility
How to control it step-by-step in PyTorch using seeds

By the end, you’ll know exactly how to make your results deterministic and reproducible, while still benefiting from randomness where it matters.

Part 8 of the 100 Days of Deep Learning with PyTorch series.

#PyTorch #DeepLearning #MachineLearning #AI #NeuralNetworks #Reproducibility #Randomness #ML