Better Interpretability Leads to Better Adoption Is your highly-trained model easy to understand? A sophisticated machine learning algorithm usually can produce accurate predictions, but its notorious “black box” nature does not help adoption at all. Think about this: If you ask me to swallow a black pill without telling me…

Image classification is the task to recognize an image. It is also called image recognition. Computer scientists have been innovative in extracting meaning from images. Its history is fascinating, though most people don’t know much about it. For this reason, I like to describe the evolution of image classification. In…

Many machine learning algorithms are complicated and not easy to understand, even though they have rendered impressive level of accuracy. As humans, we must be able to fully understand how decisions are being made so that we can trust the decisions of AI systems. We need ML models to function…

You receive a data science interview opportunity from your dream company. You have surveyed many the-top-50-question type of articles but still feel uncertain. Since there are already many similar articles, why do I dare to add an article to this crowded topic? In this article I re-write many ordinary answers…

~ “Practice makes perfect.” ~ In the previous chapter “Chapter 6: Build your transfer learning model”, we built a model for our special images. How can we improve the model performance? To get a better model performance, we can either add more data or work on the model itself (or…

We are ready to apply the transfer learning technique. This chapter will show you how to get the core part from VGG-16 and build a new neural network. You will learn the procedure to build a transfer learning model. It is the same as building any neural network. I will…

Now you are about to apply transfer learning to build an image model. What’s your topic? Maybe your model is to recognize any international cuisines or any Lego Minifigures or any actors in movies. Here comes a very practical question: how do you obtain hundreds of images to start with…

VGG-16 made its name in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) in 2014, and continues to be used by many data scientists up to now. In this chapter I show you how to load the VGG-16 model and the ResNet model. I then show you how to pre-process…

How does an algorithm see an image? How does an algorithm extract features from an image? In this chapter, I will start with the structure of image data for some of you who do not work with image data. I describe how a standard neural network classifies images of different…