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

Neural Network Architecture

Neural networks are the newest branch of Artificial Intelligence (AI). Neural networks are inspired by how the human brain works. They were invented in the 1940s by Warren McCulloch and Walter Pitts. The neural network was a mathematical model that was used to describe how the human brain can solve problems.

We will use ANN to refer to both the mathematical model, and the biological neural network when talking about the human brain.

The way a neural network learns is more complex compared to other classification or regression models. The neural network model has a lot of internal variables, and the relationship between the input and output variables may involve multiple internal layers. Neural networks have higher accuracy than other supervised learning algorithms.

Note

Mastering neural networks with TensorFlow is a complex process. The purpose of this section is to provide you with an introductory resource to get started.

In this...