3 posts found
Theory assumes infinite precision; hardware delivers float16. Bridge the gap between mathematical backprop and production systems. In this post, we cover a lot of "practical" ground from PyTorch's tape to mixed precision training, from numerical disasters to systematic testing, from gradient monitoring to interpretability. What breaks, why, and how to fix it.
Every gradient rule, from convolutions to attention, follows one pattern: the vector-Jacobian product. See past the memorized formulas to the unifying abstraction, understand how residuals and normalization tame deep networks, and learn why modern architectures are really just careful gradient engineering.
Backprop computes a million gradients for the price of two forward passes. From computational graphs to adjoints, from chain rule to a working neural network, this is the algorithm that made deep learning possible; and is demystified here step by step.