Book Image

Artificial Intelligence By Example - Second Edition

By : Denis Rothman
Book Image

Artificial Intelligence By Example - Second Edition

By: Denis Rothman

Overview of this book

AI has the potential to replicate humans in every field. Artificial Intelligence By Example, Second Edition serves as a starting point for you to understand how AI is built, with the help of intriguing and exciting examples. This book will make you an adaptive thinker and help you apply concepts to real-world scenarios. Using some of the most interesting AI examples, right from computer programs such as a simple chess engine to cognitive chatbots, you will learn how to tackle the machine you are competing with. You will study some of the most advanced machine learning models, understand how to apply AI to blockchain and Internet of Things (IoT), and develop emotional quotient in chatbots using neural networks such as recurrent neural networks (RNNs) and convolutional neural networks (CNNs). This edition also has new examples for hybrid neural networks, combining reinforcement learning (RL) and deep learning (DL), chained algorithms, combining unsupervised learning with decision trees, random forests, combining DL and genetic algorithms, conversational user interfaces (CUI) for chatbots, neuromorphic computing, and quantum computing. By the end of this book, you will understand the fundamentals of AI and have worked through a number of examples that will help you develop your AI solutions.
Table of Contents (23 chapters)
21
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22
Index

Designing datasets – where the dream stops and the hard work begins

As in the previous chapter, bear in mind that a real-life project goes through a three-dimensional method in some form or other. First, it's important to think and talk about the problem in need of solving without jumping onto a laptop. Once that is done, bear in mind that the foundation of machine learning and deep learning relies on mathematics. Finally, once the problem has been discussed and mathematically represented, it is time to develop the solution.

First, think of a problem in natural language. Then, makemathematical description of a problem. Only then should you begin the software implementation.

Designing datasets

The reinforcement learning program described in the first chapter can solve a variety of problems involving unlabeled classification in an unsupervised decision-making process. The Q function can be applied to drone, truck, or car deliveries. It...