Backpropagation as the Unifying Training Algorithm in ML
From GPT and diffusion models to AlphaFold and many brain-inspired networks, most modern machine learning systems, despite differing architectures and objectives, rely on backpropagation coupled with gradient-based optimization.
Loss Functions and Curve Fitting: Building Backprop from Scratch
To demystify backpropagation, the video starts with a simple curve-fitting problem: choosing coefficients of a polynomial to best fit a set of data points by minimizing a loss function.
Derivatives, Gradients, and the Chain Rule for Optimization
The video builds from single-variable derivatives to multivariate gradients and the chain rule, laying the mathematical groundwork behind backpropagation.
Computational Graphs and Forward/Backward Passes in Backpropagation
Backpropagation operates on computational graphs whose nodes are simple differentiable operations (addition, multiplication, nonlinearities) and whose edges carry intermediate values and gradients.
Gradient Descent and Parameter Updates in Neural Networks
Once backpropagation has provided gradients for each parameter, gradient descent (or a variant) performs the actual learning by adjusting parameters to reduce loss.