4 posts found
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.
Why does a 3-layer network solve problems a 1000-neuron single layer cannot? Understanding forward propagation, the exponential efficiency of depth, and how simple operations compose into hierarchical reasoning.
Why does walking downhill in parameter space solve everything from linear regression to GPT? A rigorous treatment of gradient descent: convergence theory, variants and the challenges of real-world optimization.
Understanding the fundamental building block of neural networks through intuition, mathematics, and implementation.