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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
Other Books You May Enjoy
22
Index

Reinforcement learning concepts

AI is constantly evolving. The classical approach states that:

  • AI covers all domains
  • Machine learning is a subset of AI, with clustering, classification, regression, and reinforcement learning
  • Deep learning is a subset of machine learning that involves neural networks

However, these domains often overlap and it's difficult to fit neuromorphic computing, for example, with its sub-symbolic approach, into these categories (see Chapter 18, Neuromorphic Computing).

In this chapter, RL clearly fits into machine learning. Let's have a brief look into the scientific foundations of the MDP, the RL algorithm we are going to explore. The main concepts to keep in mind are the following:

  • Optimal transport: In 1781, Gaspard Monge defined transport optimizing from one location to another using the shortest and most cost-effective path; for example, mining coal and then using the most cost-effective path...
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Artificial Intelligence By Example
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