This video explains gradient descent, the core algorithm for how neural networks learn, using handwritten digit recognition as an example. It details how a cost function measures network error and how gradient descent iteratively adjusts weights and biases to minimize this cost. The video also discusses the performance and limitations of basic neural networks, contrasting them with modern deep learning insights regarding memorization versus structure learning.