Everyone wants to try their hands on Machine Learning at some point of time in their software career. The first algorithm mostly all books and online courses starts with is the Linear regression. Linear arranged in a straight line. So, the idea of understanding the relationship between 2 variables by plotting a linear line is coined as linear regression. Let us take an example, Price of the house with respect to the size of the house.

Deep learning has recently seen a surge in progress, from training shallow networks to very deep networks consisting of tens to hundreds of layers. Deep neural networks (DNNs) have demonstrated success for many supervised learning tasks (Szegedy et al., 2015; Simonyan and Zisserman, 2014). The focus has been on increasing accuracy, in particular for image, speech, and recently text tasks, where deep convolutional neural networks (CNNs) are applied. The resulting networks often include millions to billions parameters. Having too many parameters, increases the risk of over-fitting and hence a poor model generalization afterall.

Recently, during my coursework, I have been left in awe of the advancements in the field of science in general. In just about a decade, we have completely revolutionized the way we look at the capabilities of machines, the way we build software and much more. Tasks that seemed impossible just a decade ago have become accessible and effortless. Long story short, we have made the machines think!! Sounds cool, isn't it? Artificial Intelligence has truly entered the mainstream consciousness.