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

Optimizing Your Solutions with K-Means Clustering

No matter how much we know, the key point is the ability to deliver an artificial intelligence (AI) solution. Implementing a machine learning (ML) or deep learning (DL) program remains difficult and will become more complex as technology progresses at exponential rates.

There is no such thing as a simple or easy way to design AI systems. A system is either efficient or not, beyond being either easy or not. Either the designed AI solution provides real-life practical uses, or it builds up into a program that fails to work in various environments beyond the scope of its training sets.

This chapter doesn't deal with how to build the most difficult system possible to show off our knowledge and experience. It faces the hard truth of real-life delivery and ways to overcome obstacles. For example, without the right datasets, your project will never take off. Even an unsupervised ML program requires reliable data in...