This video provides an intuitive explanation of backpropagation, the fundamental algorithm for training neural networks. It details how desired adjustments to network outputs are propagated backward through layers, influencing weights and biases based on their sensitivity to the cost function. The video also introduces stochastic gradient descent for efficient learning and emphasizes the crucial role of extensive labeled training data in machine learning.