Backpropagation in Machine Learning

In this video, Artem Kirsanov delves into the backpropagation algorithm, a foundational element in the field of machine learning. Backpropagation is used in various machine learning systems, from GPT and MidJourney to AlphaFold, to train models by minimizing a loss function. The video provides a historical background, noting contributions from Seppo Linnainmaa and the influential 1986 paper by David Rumelhart, Geoffrey Hinton, and Ronald Williams. Artem explains the concept of curve fitting, where a polynomial function is adjusted to best fit a set of data points by minimizing the loss function, which measures the square distance between the data points and the curve. The video introduces the concept of derivatives and gradient descent, a method for finding the minimum of a loss function by iteratively adjusting parameters in the direction opposite to the gradient. The tutorial also covers higher dimensions, the chain rule, and computational graphs, which are used to calculate derivatives efficiently. Artem emphasizes that backpropagation enables the training of complex models by breaking down the optimization problem into simpler, differentiable operations. The video concludes with a teaser for the next part, which will explore synaptic plasticity and learning in biological neural networks, questioning whether the brain uses similar optimization techniques.