Fitting a Neural Network Using Randomized Optimization in Python

Python's mlrose package provides functionality for implementing some of the most popular randomization and search algorithms, and applying them to a range of different optimization problem domains. In this tutorial, we will discuss how mlrose can be used to find the optimal weights for machine learning models, such as neural networks and regression models. That is, to solve the machine learning weight optimization problem. This is the third in a series of three tutorials about using mlrose to solve randomized optimization problems. Part 1 can be found here and Part 2 can be found here. For a number of different machine learning models, the process of fitting the model parameters involves finding the parameter values that minimize a pre-specified loss function for a given training set.