WHY Inferential Statistics and Hypothesis testing?? In today’s world, with the abundance in data, every organization wishes to optimize on their products by making it more customer centric. To do that, they need to develop an understanding of the population. In order to understand the population, companies need to categorize customers based on certain parameters like age, education, location, life style, income and so on. But in most cases, it is extremely difficult to collect data for the entire population. That is where the concept of samples and sampling comes into the picture. Sampling is the process of selecting a subset from a population that would be representative of the population. The idea is to perform the analysis on a sample and make inferences about the population. Let us look at a simple example to understand this concept. Consider a case where we want to find the average height of men and women in the city of Cincinnati and compare it to a value that is cl
Deep Learning!! I have always wondered about the concept of perception i.e. how does our brain understand the objects we see? How does it know the difference between a cat and a dog? How are we able to differentiate people from each other? The most amazing thing is how does the brain identify the same person over the years even when they age? I haven't got answers to most of these questions, but what if I say there are technologies out there today that can do this same task as a human being in terms of perceiving objects? Yes, it has become a reality!! Take for example the iPhone X's face recognition based locking system. Isn't is amazing? There are many more applications of the same technology. When we dig a bit deep into what is the most commonly used technique in these applications, we find that the answer is neural networks. To be more specific, people have started using a neural network based technique called Deep Learning extensively for this purpose. No