One-vs-All Logistic Regression for Image Recognition in Python

This article represents the continuation of a series of dedicated articles that began some time ago. This series proposes the reader to understand the basic concepts leading to Machine Learning for biomedical data, like the difference between Linear and logistic regression, the Cost Function, Regularized Logistic Regression, and Gradient (see the Reference section). Each implementation is intended from scratch, and we will not use optimized machine learning packages like Scikit-learn, PyTorch, or TensorFlow. The only requirement is an updated version of Python 3, some fundamental libraries, and the desire to read this post to the end! Regressions (linear, logistic, for single and multiple variables) are statistical models helpful in finding correlations between observed dataset variables and answering whether those correlations are statistically significant.