If you want to gain a sound understanding of machine learning then you must know gradient descent optimization. In this article, you will get a detailed and intuitive understanding of gradient descent to solve machine learning algorithms. The entire tutorial uses images and visuals to make things easy to grasp. Here, we will use an example
The Beautiful Formula Mathematicians often conduct competitions for the most beautiful formulae of all. The first position, almost every time, goes to the formula discovered by Leonhard Euler. Displayed below is the formula. This formula is phenomenal because it is a combination of the five most important constants in mathematics i.e. 0 : Additive Identity 1 :
A Primer on Logistic Regression – Are you Happy? A few years ago, my wife and I took a couple of weeks’ vacation to England and Scotland. Just before boarding the British Airway’s plane, an air-hostess informed us that we were upgraded to business class. Jolly good! What a wonderful start to the vacation. Once
Welcome back to our retail case study example for marketing analytics. In the previous 8 parts, we have covered some of the key tasks of data science such as: Problem definition: Part 1 & Part 2 Description: Part 3 Association: Part 4 Classification: Part 5, Part 6, Part 7 & Part 8 In this part, we will learn about estimation
Welcome back to the deep learning example to build an OCR application. The idea of this simple application is to identify numbers in an image of written text. In the last part, we used three different models and got the following accuracy for identification of the test images: Model 1 – Logistic regression: 92% accuracy
Facebook was a major sensation and a source of great amusement in a British country house in the early 20th century. It was such a big hit that it got a special mention in a newspaper published in the year 1902. Facebook, then, of course, had a completely different meaning than the online social media we
Welcome back to this series of articles on deep learning and neural networks. In the last part, you learned how training a deep learning network is similar to a plumbing job. This time you will learn the math of deep learning. We will continue to use the plumbing analogy to simplify the seemingly complicated math. I
Predictive analytics is considered one of the sexiest professions of our times. But where did it all start? What was the first business application of predictive analytics? It is hard to pin down one particular application but one of the earliest and highly successful applications is certainly credit risk models and retail credit scorecards. Credit scorecards help banks assess
How do machines learn? They learn the same way as humans. Humans learn from experience and so do machines. For machines, the experience is in the form of data. Machines use powerful algorithms to make sense of the data. They identify underlining patterns within the data to learn things about the world. Like humans, machines
Are you preparing for data science interviews? To help you in your preparation, in this article, I will discuss some of the worst mistakes candidates make in data science interviews. In the previous article, we discussed the structure of data science interviews along with sample questions and preparation strategy. Many times I have noticed that well-prepared