Sharpen your NumPy skills while learning Logistic Regression What’s our plan for implementing Logistic Regression in NumPy? Let’s first think of the underlying math that we want to use. There are many ways to define a loss function and then find the optimal parameters for it, among them, here we Read more…
Understand better linear regression & sharpen your NumPy skills Let’s first briefly recall what linear regression is: Linear regression is estimating an unknown variable in a linear fashion based on some other known variables. Visually, we fit a line (or a hyperplane in higher dimensions) through our data points. If Read more…
From zero to a working implementation Artificial neural networks, the subject of our article, are mathematical models that are inspired by biological neural networks and are attempting to imitate them. As biological neural networks are a combination of more neurons aimed at a specific task, artificial neural networks are a Read more…
Understanding the building block of Neural Networks The perceptron is the building block of artificial neural networks, it is a simplified model of the biological neurons in our brain. A perceptron is the simplest neural network, one that is comprised of just one neuron. The perceptron algorithm was invented in Read more…
Implementing a Maximum Likelihood Classifier and using it to predict heart disease What is this thing about? The main idea of Maximum Likelihood Classification is to predict the class label y that maximizes the likelihood of our observed data x. We will consider x as being a random vector and y as being a parameter (not random) Read more…