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Overview: This book presents a set of lectures on Python programming for economics and finance. Substantial parts of Machine Learning and Artificial Intelligence are about: • approximating an unknown function with a known function; • estimating the known function from a set of data on the left- and right-hand variables. This lecture describes the structure of a plain vanilla Artificial Neural Network (ANN) of a type that is widely used to approximate a function f that maps x in a space X into y in a space Y. To introduce elementary concepts, we study an example in which x and y are scalars. We’ll describe the following concepts that are brick and mortar for neural networks: • a neuron; • an activation function; • a network of neurons; • A neural network as a composition of functions; • back-propagation and its relationship to the chain rule of differential calculus. We describe a “deep” neural network of “width” one. Deep means that the network composes a large number of functions organized into nodes of a graph. Width refers to the number of right hand side variables on the right hand side of the function being approximated. Setting “width” to one means that the network composes just univariate functions. Linear regression is a standard tool for analyzing the relationship between two or more variables. In this lecture, we’ll use the Python package statsmodels to estimate, interpret, and visualize linear regression models.
Genre: Non-Fiction > Tech & Devices
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