#006A Fast Logistic Regression Master Data Science

When we are programming Logistic Regression or Neural Networks we should avoid explicit \(for \) loops. It's not always possible, but when we can, we should use built-in functions or find some other ways to compute it. Vectorizing the implementation of Logistic Regression makes the code highly efficient. In this post we will see how we can use this technique to compute gradient descent without using even a single \(for \) loop. This code was non-vectorized and highly inefficent so we need to transform it.