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Artificial Intelligence By Example

Artificial Intelligence By Example - Second Edition

By : Denis Rothman
4.6 (17)
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Artificial Intelligence By Example

Artificial Intelligence By Example

4.6 (17)
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)
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21
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Index

Building a Reward Matrix – Designing Your Datasets

Experimenting and implementation comprise the two main approaches of artificial intelligence. Experimenting largely entails trying ready-to-use datasets and black box, ready-to-use Python examples. Implementation involves preparing a dataset, developing preprocessing algorithms, and then choosing a model, the proper parameters, and hyperparameters.

Implementation usually involves white box work that entails knowing exactly how an algorithm works and even being able to modify it.

In Chapter 1, Getting Started with Next-Generation Artifcial Intelligence through Reinforcement Learning, the MDP-driven Bellman equation relied on a reward matrix. In this chapter, we will get our hands dirty in a white box process to create that reward matrix.

An MDP process cannot run without a reward matrix. The reward matrix determines whether it is possible to go from one cell to another, from A to B. It is like a map of a city that...

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Artificial Intelligence By Example
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