Universal Approximation Theorem: MLP

This lecture covers Multi-Layer Perceptrons (MLP) and their application from classification to regression, including the Rosenbrock function. It discusses the deep network's function implementation, regression in everyday life, non-linear regression, and the Rosenbrock function regression. The lecture explores the accuracy as a function of the number of nodes, image representation, and the transition from MLP to deep learning, including PyTorch translation. It delves into the power of composition, the Universal Approximation Theorem in 1D and nD, and the complexity of surfaces. The lecture also touches on overfitting, stochastic gradient descent, weight decay, interpolation vs. extrapolation, backpropagation, partial derivatives, and the challenges of vanishing and exploding gradients.