In this tutorial, you will learn to implement Linear Regression for prediction using Numpy in detail and also visualize how the algorithm learns epoch by epoch. In addition to this, you will explore two layer Neural Networks.
In the previous tutorial, you got a very brief overview of a perceptron.
In this tutorial, you will dig deep into implementing a Linear Perceptron (Linear Regression) from which you’ll be able to predict the outcome of a problem!
This tutorial will apparently include a bit more of math as it is inevitable, but there’s no need to worry as I will explain them ground up. Regardless of this, it must be realized that all machine learning algorithms are basically mathematical formulations which are finally implemented in the form of code.
Before we start off, remember that we had used the threshold activation function to mimic the function of AND and NOR Gates?!
