Book Image

The Applied Artificial Intelligence Workshop

By : Anthony So, William So, Zsolt Nagy
Book Image

The Applied Artificial Intelligence Workshop

By: Anthony So, William So, Zsolt Nagy

Overview of this book

You already know that artificial intelligence (AI) and machine learning (ML) are present in many of the tools you use in your daily routine. But do you want to be able to create your own AI and ML models and develop your skills in these domains to kickstart your AI career? The Applied Artificial Intelligence Workshop gets you started with applying AI with the help of practical exercises and useful examples, all put together cleverly to help you gain the skills to transform your career. The book begins by teaching you how to predict outcomes using regression. You will then learn how to classify data using techniques such as k-nearest neighbor (KNN) and support vector machine (SVM) classifiers. As you progress, you’ll explore various decision trees by learning how to build a reliable decision tree model that can help your company find cars that clients are likely to buy. The final chapters will introduce you to deep learning and neural networks. Through various activities, such as predicting stock prices and recognizing handwritten digits, you’ll learn how to train and implement convolutional neural networks (CNNs) and recurrent neural networks (RNNs). By the end of this applied AI book, you’ll have learned how to predict outcomes and train neural networks and be able to use various techniques to develop AI and ML models.
Table of Contents (8 chapters)
Preface

The Mean Shift Algorithm

Mean shift is a hierarchical clustering algorithm that assigns data points to a cluster by calculating a cluster's center and moving it towards the mode at each iteration. The mode is the area with the most data points. At the first iteration, a random point will be chosen as the cluster's center and then the algorithm will calculate the mean of all nearby data points within a certain radius. The mean will be the new cluster's center. The second iteration will then begin with the calculation of the mean of all nearby data points and setting it as the new cluster's center. At each iteration, the cluster's center will move closer to where most of the data points are. The algorithm will stop when it is not possible for a new cluster's center to contain more data points. When the algorithm stops, each data point will be assigned to a cluster.

The mean shift algorithm will also determine the number of clusters needed, in contrast...